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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="author" content="Akbar Latif" />
<title>Predictive Modeling and Bayesian Inference</title>
<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
});
</script>
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yr6EIj+LzNj/mfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h/C/PkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn/pdpgHKNGrGIdkRK+KPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho+EIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA+q/j9m3LM/O7WJka4tSidVCjsvo2lQ/2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3/5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho/bcwQdeboqfQartuU3CsCf+cXkgYAqp/0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid/NIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A+TRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC+JE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW/WDHA60cYFaI/PjpzquUqdaYGcIq+mLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC+1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A+P/oFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E/vwOiKxRtCWsDM+eTHUrmwrCK5BIfMzGkD+0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3/kFutpQGNc3pCR7gvC4sgwbupDu3DyEN+W6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc+h1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0+bC5zgpGz7Io+mjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO/ENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn+3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif+pZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx/k3QgnAFSq27/2i4GEBA+UvTJKK/9eISNvG46Em5RZfjTYLdeD8kdXHyrwId/DQZUaMCY4gGbke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS/2ToPjqkaq62/7WFG8advGlRRqxB9diP07JrXowKR9tpRa+jGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq+nsp3YMuXt/GkXxLx/P6+ZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar+gMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud+YlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl/zh575R5rsrmRnKAzq4POFdgbYBuEviM4+LVC15ssLNFghbTtHWerS1hDt5s4qkLUha/qpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI+yjEldJfSo4y0QhG4i4IwkRFGcjWY8+EzgYYJUK7BXQksLxAww/YYWBMhJILB9e8ePEJ4OP7z+4/wOQDl64iOYDp26DaONPxpKtBxq/aTzRGarm3VkPYTLJKx6Z/Mw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ+lPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k/UuGkNpP1DBI5ch/EehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv+J41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI+HYexTUevLUeta4/DqKrbMVS+Yqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ++KkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9/Wx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k/90B8+yRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB/mQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go/n6j0cS+a2gEU8gIHJ+BwfgZX4GL+Bd/gW34FZ+BS/gUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh/F062yJ7AAAAEDAWAAABWhJ+KPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg) 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<h1 class="title toc-ignore">Predictive Modeling and Bayesian
Inference</h1>
<h4 class="author">Akbar Latif</h4>
</div>
<div id="part-1-3d-printer" class="section level1" number="1">
<h1><span class="header-section-number">1</span> Part 1: 3D printer</h1>
<p>In this part we will look at <span class="math inline">\(\texttt{filament1}\)</span> data from a 3D printer
that uses rolls of <span class="math inline">\(\textit{filament}\)</span> that get heated and
squeezed through a moving nozzle, gradually building objects. The
objects are designed in CAD that estimates how much material will be
required to print them. The data file <code>"filament1.rda"</code>
contains information about one 3D-printed object per row. The columns
are:</p>
<ul>
<li><span class="math inline">\(\texttt{Index:}\)</span> an observation
index</li>
<li><span class="math inline">\(\texttt{Date:}\)</span> printing
dates</li>
<li><span class="math inline">\(\texttt{Material:}\)</span> the printing
material, identified by its colour</li>
<li><span class="math inline">\(\texttt{CAD_Weight:}\)</span> the object
weight (in grams) that the CAD software calculated</li>
<li><span class="math inline">\(\texttt{Actual_Weight:}\)</span> the
actual weight of the object (in grams) after printing.</li>
</ul>
<p>We are considering two linear models, A and B, for capturing the
relationship between <span class="math inline">\(\textsf{CAD_Weight}\)</span> and <span class="math inline">\(\textsf{Actual_Weight}\)</span>. Denote the <span class="math inline">\(\textsf{CAD_weight}\)</span> for observation <span class="math inline">\(i\)</span> as <span class="math inline">\(x_i\)</span>, and the corresponding <span class="math inline">\(\textsf{Actual_Weight}\)</span> as <span class="math inline">\(y_i\)</span>. As in Project 1, the two models are
defined by:</p>
<ul>
<li>Model A: <span class="math inline">\(y_i ∼ \text{Normal} [β_1 +
β_2x_i, \exp(β_3 + β_4x_i)]\)</span></li>
<li>Model B: <span class="math inline">\(y_i ∼ \text{Normal} [β_1 +
β_2x_i, \exp(β_3) + \exp(β_4)x_i^2 ]\)</span>.</li>
</ul>
<div id="prediction" class="section level2" number="1.1">
<h2><span class="header-section-number">1.1</span> Prediction</h2>
<p>Now using these models and the function
<code>filament1_predict</code> I will compute the probabilistic
prediction distributions, and 95% significance prediction intervals
where the function will output the meand and standard deviations of each
item and then give the lwr and upper bounds of the 95% prediction
intervals. I will then run the code and display the predictive plots for
model A and model B.</p>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
<p>The plot clearly shows that the two models esitmate the <span class="math inline">\(\texttt{CAD_Weight}\)</span> quite well when the
weight is lower, as it is almost completely linear on the <span class="math inline">\(y = x\)</span> line, but as it gets bigger, the
variance in estimation grows as well. We can also see that all mean
values are in the prediction interval which tells us that both models
are consistent in their predictions as well as the models being precise
in their predicitions. The prediction interval for the lower CAD_weights
shows a high level of precision and certainty, but as the CAD_Weights
grow the prediction interval grows for both models showing that the
predicitons are more uncertain for higher weights. Also notice that
model A has a bigger prediction interval than B, so A has a higher level
of uncertainty than B.</p>
</div>
<div id="scoring" class="section level2" number="1.2">
<h2><span class="header-section-number">1.2</span> Scoring</h2>
<p>The Standard Error (SE) and Dawid-Sebastiani (DS) are two different
ways of data scoring.</p>
<p>Squared error scoring is a commonly used metric in regression
analysis to measure the average squared difference between the predicted
values and the actual values in a dataset. It is the difference in the
actual value minus predicted mean for the value by the model squared
i.e.,</p>
<p><span class="math display">\[ \text{SE}: S_{SE}(F,y) = (y -
\widehat{y}_F) \]</span></p>
<p>Where:</p>
<ul>
<li><span class="math inline">\(y\)</span> is the true value</li>
<li><span class="math inline">\(\hat{y}_F\)</span> is the predicted mean
at that <span class="math inline">\(y\)</span></li>
</ul>
<p>Squared error scoring is preferred in many contexts because it
penalizes larger errors more heavily than smaller errors due to the
squaring operation. This means that larger deviations between predicted
and actual values contribute proportionally more to the overall score,
making the metric sensitive to outliers or large errors in
prediction.</p>
<p>Dawid-Sebastiani’s method of scoring is another method for scoring
used in the analysis of models.</p>
<p><span class="math display">\[DS: S_{DS}(F,y) = \frac{(y -
\mu_F)^2}{\sigma_F^2} + \log(\sigma_F^2) \]</span></p>
<p>Where:</p>
<ul>
<li><span class="math inline">\(y\)</span> is the true value</li>
<li><span class="math inline">\(\mu_F\)</span> is the predicted mean at
that <span class="math inline">\(y\)</span> by the model F</li>
<li><span class="math inline">\(\sigma_F^2\)</span> is the predicted
variance at that <span class="math inline">\(y\)</span> by the model
F</li>
</ul>
<p>Both of these methods for scoring are good methods for telling how
good models are on accuracy and consistency on accurately predicting
data and can be coded by the functions shown below.</p>
<pre class="r"><code>#' square_error_score
#'
square_error_score <- function(prediction){
score <- prediction %>%
mutate(
se = ((Actual_Weight - mean))^2)
}
#' ds_score
#'
ds_score <- function (prediction){
score <- prediction %>%
mutate(
ds = (Actual_Weight - mean)^2/sd^2 + 2 * log(sd))
}</code></pre>
</div>
<div id="leave-one-out" class="section level2" number="1.3">
<h2><span class="header-section-number">1.3</span> Leave One Out</h2>
<p>Leave-one-out cross-validation(LOOCV) is a way of testing how
accurate a model is at predicting data. The technique leaves one data
point out at a time and runs the model without it then check the
predicted data against the left out point. Then the process is repeated
for the rest of the datasetn and then combined with all the other tests
into a summary. This technique is useful with smaller data sets as with
larger ones the performing this can take a long time as you have to
repeatedly run the model for each data point. It is an unbiased testing
method as the data is compared against itself. 1 In this task we
performed leave-one-out cross-validation as returned the averge Square
Error and Dawid-Sebastiani scores for each model as you can see in the
table below:</p>
<table>
<thead>
<tr class="header">
<th align="left">Model</th>
<th align="right">Average.Square.Error</th>
<th align="right">Average.DS.Score</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">A</td>
<td align="right">1.841734</td>
<td align="right">1.128348</td>
</tr>
<tr class="even">
<td align="left">B</td>
<td align="right">1.841995</td>
<td align="right">0.939715</td>
</tr>
</tbody>
</table>
<p>When looking at the table above the average Square Error shows that
the models are quite similar but we can expect this since the means for
both models are the same, so the square error scores do not tell us too
much about the models. The Dawid-Sebastiani scores are different as we
know that they rely on variance of the models in their calculations, so
when looking at the different scores we can see that Model B is better
in its prediction since it gives a lower averge DS score. We know that
for DS scores the lower is a better predictor with a higher
accuracy.</p>
<p><img role="img" 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" width="672" /></p>
<p>We can look at these two graphs of the scores in the LOOCV and
conclude that the models are quite similar in their estimation. Looking
at SE the scores are almost identical in the lower CAD_Weights, but in
the higher weights the error grows and overall it looks like model B
performs slightly better. In Looking as the DS scores we can conclude
that model B is slightly better as is varies less where A will jump
quite a bit and have higher jumps in error than B.</p>
</div>
<div id="monte-carlo-methods-for-p-values" class="section level2" number="1.4">
<h2><span class="header-section-number">1.4</span> Monte-Carlo methods
for p-Values</h2>
<p>Using Monte-Carlo estimation we can find the p-value of the scores to
test the interchangability between methods of scoring. We computed the p
values for the models using the LOOCV data and a sample size of
<code>N = 10000</code> to test the null hypothesis that the mdoels are
interchangable versus the alternate hypothesis that model B outperforms
model A. The results of the tesing are displayed below:</p>
<p>pairwise exchangable</p>
<table>
<thead>
<tr class="header">
<th align="left">Score</th>
<th align="right">se</th>
<th align="right">ds</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">p value:</td>
<td align="right">0.4989</td>
<td align="right">0.0436</td>
</tr>
</tbody>
</table>
<p>We can see from the table that the p value associated with the SE
score is 0.4989 which is high enough to lead us to fail to reject the
null hypothesis. Conversely the p value associated with the DS score is
small enough at 0.0436 to allow us to reject the null hypothesis since
it is lower than 0.05. We can point this difference in p value to many
different factors. First and foremost is the fact that the DS score
relies on the variance of the models which the SE score does not, so the
p value for DS may account for that allowing it to be smaller. We can
continue analysis futher by looking at the standard errors of these
values:</p>
<table>
<thead>
<tr class="header">
<th align="left">Score</th>
<th align="right">se</th>
<th align="right">ds</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Standard Error:</td>
<td align="right">0.005</td>
<td align="right">0.002042</td>
</tr>
</tbody>
</table>
<p>In looking at the standard errors in the above table we can conclude
that the the DS p value is better as its standard error is less than
half of the SE standard error. Finally a thing to note about these
exchangability is that this test looks at pairwise exchanges and not
total model exchages as we are using the LOOCV data.</p>
</div>
</div>
<div id="part-2-archaeology-in-the-baltic-sea" class="section level1" number="2">
<h1><span class="header-section-number">2</span> Part 2: Archaeology in
the Baltic sea</h1>
<p>In this question we are looking at estimating how many people were
buried in a gravesite based upon the amount of femurs found in the site.
We observe the number of left and right femurs show as <span class="math inline">\((y_1 = 256)\)</span> and <span class="math inline">\((y_2 = 237)\)</span> where <span class="math inline">\(y_1\)</span> represents the number of left femurs
and <span class="math inline">\(y_2\)</span> represents the number of
right femurs.</p>
<p>We build this model from a BIN<span class="math inline">\((N,
\phi)\)</span> distribuition. Where <span class="math inline">\(N\)</span> is total number of people buried and
<span class="math inline">\(\phi\)</span> is the probability of finding
a femur, left or right, or both. In this model both <span class="math inline">\(N\)</span> and <span class="math inline">\(\phi\)</span> are unknown parameters. The
probability funciton for a single observation $y (N, ) $ is <span class="math display">\[
p(y|N,\phi) = {N\choose y} \phi^y (1- \phi)^{N-y}.
\]</span> The combined <span class="math inline">\(\textit{log-likelihood} \,\, l(y_1,y_2|N,\phi) =
\log p(y_1,y_2|N,\phi)\)</span> for the data set <span class="math inline">\(\{y_1,y_2\}\)</span> is given by <span class="math display">\[
\begin{align*}
l(y_1,y_2|N, \phi) &= -\log\Gamma(y_1 +1) - \log\Gamma(y_2 +1)\\
&- \log\Gamma(N - y_1 + 1) - \log\Gamma(N-y_2+1) +
2\log\Gamma(N+1)\\
&+ (y_1 + y_2)\log(\phi) + (2N - y_1 -y_2)\log(1-\phi)
\end{align*}
\]</span></p>
<p>The archeologist belived that there were 1000 femurs in the sight and
that they would find about half, so we encode this belief through
Bayesian analysis. We use <span class="math inline">\(\xi =
1/(1+1000)\)</span>, which corresponds to an expected 1000 and <span class="math inline">\(a = b = 2\)</span>, which makes <span class="math inline">\(\phi\)</span> more likely to be close to 1/2 than
to 0 or 1.</p>
<p>We now model the posterior probability for <span class="math inline">\((N, \phi|y)\)</span> <span class="math display">\[
p_{N, \phi|y}(n, \phi|y) = \frac{p_{N, \phi|y}(n,\phi,y)
}{p_y(\mathbf{y})} = \frac{p_N(n)p_\phi(\phi)p(y|n,\phi)}{p_y(y)}
\]</span></p>
<p>Let N have a Geom(<span class="math inline">\(\xi\)</span>), $>0
$, prior and <span class="math inline">\(\phi\)</span> have a Beta(<span class="math inline">\(a,b\)</span>), <span class="math inline">\(a,b
>0\)</span> prior, <span class="math display">\[
\begin{gather*}
p_N(n)= P(N=n) = \xi (1-\xi)^n,\quad n = 0,1,2,3...,\\
p_\phi(\phi) = \frac{\phi^{a-1}(1-\phi)^{b-1}}{B(a,b)}, \quad \phi \in
[0,1].
\end{gather*}
\]</span></p>
<p>We are now going to use Monte Carlo integration using the above prior
distributions and sames distrubuted as <span class="math inline">\(n^{[k]} \sim \text{Geom}(\xi)\)</span> and <span class="math inline">\(\phi^{[k]} \sim \text{Beta}(a,b)\)</span> and used
to compute the Monte Carlo estimates:</p>
<p><span class="math display">\[\begin{align*}
\widehat{p}_y(y) &= \frac{1}{K}\sum\limits_{k=1}^Kp(y|n^{[k]},
\phi^{[k]})\\
\widehat{E}(N|y)
&= \frac{1}{K\widehat{p}_y(y)}\sum\limits_{k=1}^K
n^{[k]}p(y|n^{[k]}, \phi^{[k]}) \\
\widehat{E}(\phi|y)
&= \frac{1}{K\widehat{p}_y(y)}\sum\limits_{k=1}^K
\phi^{[k]}p(y|n^{[k]}, \phi^{[k]})
\end{align*}\]</span></p>
<p>Where <span class="math inline">\(\widehat{p}_y(y)\)</span> is our
normalizing constant for the prior distribution. <span class="math inline">\(\widehat{E}(N|y)\)</span> is our expected number
of buried people given the observations, and $(|y) $ is our expected
probability of finding a femur.</p>
<p>We now run the <code>estimate</code> code with the data <span class="math inline">\(y_1 = 237,\, y_2 = 256\)</span> and <span class="math inline">\(a = b= 0.5\)</span> and for 10000 samples. And
display the results in the table below.</p>
<table>
<thead>
<tr class="header">
<th align="right">p_y</th>
<th align="right">E_N_given_y</th>
<th align="right">E_phi_given_y</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">7.9e-06</td>
<td align="right">981.9492</td>
<td align="right">0.3741221</td>
</tr>
</tbody>
</table>
<p>Our result show us that there were an expected 982 bodies buried, and
we had a probability of 0.374 of finding a femur. Since the scienctists
believed that there were 1000 bodies buried, our result of 982 is not
that far off. Altough our <span class="math inline">\(\phi\)</span>
results given the observations is much lower. This result shows that the
monte carlo integration is slightly off for calculating the phi. For
further analysis we would need ot compute the standard error and
determine where the error is. Another reason for finding so few could be
in the fact that the bodies were buried so long ago.</p>
</div>
<div id="code-appendix" class="section level1" number="3">
<h1><span class="header-section-number">3</span> Code appendix</h1>
<pre class="r"><code>#' Akbar Latif
#' neg_log_lik
#
#' @description Evaluate the negated log-likelihood for model A and B
#' @param beta A vector with the beta parameters
#' @param data A `data.frame` with the same variables as the `filament1` data set.
#' Must have columns `CAD_Weight` and `Actual_Weight`
#' @param model Either "A" for a log-linear variance model, or "B" for a proportional
#' scaling error model
neg_log_lik <- function(beta, data, model){
mu <- beta[1] + beta[2]*data[["CAD_Weight"]]
# distinguish between the two models to find the particular standard deviation for the betas
if(model == "A") {
sigma <- sqrt(exp(beta[3] + beta[4]*data[["CAD_Weight"]]))
}else{
sigma <- sqrt(exp(beta[3])+exp(beta[4]) * (data[["CAD_Weight"]]^2))
}
- sum(dnorm(data[["Actual_Weight"]],
mean = mu,
sd=sigma,
log = TRUE))
}
#' filament_estimate
#
#' @description Estimate filament models with different variance structure
#' @param data A `data.frame` with the same variables as the `filament1` data set.
#' Must have columns `CAD_Weight` and `Actual_Weight`
#' @param model Either "A" for a log-linear variance model, or "B" for a proportional
#' scaling error model
#' @return An estimation object suitable for use with [filament1_predict()]
filament1_estimate <- function(data, model) {
model <- match.arg(model, c("A", "B"))
if (model == "A") {
beta_start <- c(-0.1, 1.07, -2, 0.05)
} else {
beta_start <- c(-0.15, 1.07, -13.5, -6.5)
}
opt <- optim(beta_start,
neg_log_lik,
data = data,
model = model,
hessian = TRUE,
method = "Nelder-Mead",
control = list(maxit = 5000)
)
fit <- list(
model = model,
par = opt$par,
hessian = opt$hessian
)
class(fit) <- c("filament1_estimate", "list")
fit
}
#' filament1_aux_EV
#'
#' @description Evaluate the expectation and variance for model A and B
#' @param beta A vector with the beta parameters
#' @param data A `data.frame` containing the required predictors, including `CAD_Weight`
#' @param model Either "A" for a log-linear variance model, or "B" for a proportional
#' scaling error model
#' @param Sigma_beta : If not NULL, an estimate of the covariance matrix for
# the uncertainty of estimated betas
#' @return A list with four elements:
# E : E(y|beta,x)
# V : Var(y|beta,x)
# VE : Var(E(y|beta,x)|x) or NULL
# EV : E(Var(y|beta,x)|x) or NULL
filament1_aux_EV <- function(beta, data, model = c("A", "B"),
Sigma_beta = NULL) {
model <- match.arg(model)
if (model == "A") {
ZE.0 <- model.matrix( ~ 1 + CAD_Weight, data = data)
ZV.0 <- model.matrix( ~ 1 + CAD_Weight, data = data)
ZE = cbind(ZE.0, ZV.0 * 0)
ZV = cbind(ZE.0 * 0, ZV.0)
VE <- EV <- NULL
if (!is.null(Sigma_beta)) {
# E(Var(y|beta,x)|x)
EV <- exp(ZV %*% beta + rowSums(ZV * (ZV %*% Sigma_beta)) / 2)
# Var(E(y|beta,x)|x)
VE <- rowSums(ZE * (ZE %*% Sigma_beta))
}
out <- list(
E = ZE %*% beta,
V = exp(ZV %*% beta),
VE = VE,
EV = EV
)
} else {
ZE.0 <- model.matrix( ~ 1 + CAD_Weight, data = data)
ZV.0 <- model.matrix( ~ 1 + I(CAD_Weight^2), data = data)
ZE = cbind(ZE.0, ZV.0 * 0)
ZV = cbind(ZE.0 * 0, ZV.0)
VE <- EV <- NULL
if (!is.null(Sigma_beta)) {
# E(Var(y|beta,x)|x)
# (pmin: Ignore large Sigma_beta values)
EV <- ZV %*% exp(beta + pmin(0.5^2, diag(Sigma_beta)) / 2)
# Var(E(y|beta,x)|x)
VE <- rowSums(ZE * (ZE %*% Sigma_beta))
}
out <- list(
E = ZE %*% beta,
V = ZV %*% exp(beta),
VE = VE,
EV = EV
)
}
out
}
#' filament1_predict
#'
#' @description in this function will create a predictive data of the data based on newdata
#' @param data the old data to be predicted upon
#' @param model which model is being used
#' @param newdata the newdata which is being used to predict against the old data
#'
#' @return
filament1_predict <- function(data, model = c("A", "B"), newdata){
beta <- filament1_estimate(data, model)
Sigma_beta <- solve(beta$hessian)
EV <- filament1_aux_EV(beta$par, newdata, model, Sigma_beta)
sd = sqrt(EV$EV + EV$VE)
# Compute 95% prediction intervals
alpha = 0.05
lwr <- EV$E - qnorm(1 - (alpha/2))*sd
upr <- EV$E + qnorm(1 - (alpha/2))*sd
# Create data frame to store results
results <- data.frame(
mean = EV$E,
sd = sd,
lwr = c(lwr),
upr = c(upr)
)
return(results)
}
#' square_error_score
#'
#' @description
#' @param prediction a data frame with Actual_Weght and mean
#' @return the prediction data frame with a new column
square_error_score <- function(prediction){
score <- prediction %>%
mutate(
se = ((Actual_Weight - mean))^2)
}
#' ds_score
#'
#' @description
#' @param prediction a data frame with Actual_Weght and mean and sd
#' @return the prediction data frame with a new column
ds_score <- function (prediction){
score <- prediction %>%
mutate(
ds = (Actual_Weight - mean)^2/sd^2 + 2 * log(sd))
}
#' leave1out
#'
#' @description
#' @param data the data
#' @param model which model is being used
#' @return the data frame with the
leave1out <- function(data, model = c("A","B")){
data_new <- data %>% mutate(mean = NA_real_, sd = NA_real_, se = NA_real_, ds = NA_real_)
for (i in seq_len(nrow(data_new))) {
pred <- filament1_predict(data_new[-i, , drop = FALSE], model, data_new[i,])
data_new[i, "mean"] = pred$mean
data_new[i, "sd"] <- pred$sd
}
data_final <- data_new %>% mutate(
se = ((Actual_Weight - mean))^2,
ds = (Actual_Weight - mean)^2/sd^2 + 2 * log(sd))
return(data_final)
}
#' monte_p_value
#'
#' @description Compute p values using monte carlo methods
#' @param dataA the first bit of data to use
#' @param dataB the second bit of data to use
#' @param N the number of samples to be used
#' @return a data frame with the computed se and ds p values
Monte_p <- function(dataA, dataB, N){
score_diff <- data.frame(se = dataA$se - dataB$se,
ds = dataA$ds - dataB$ds)
statistic0 <- score_diff %>% summarise(se = mean(se), ds = mean(ds))
statistic <- data.frame(se = numeric(N),
ds = numeric(N))
for (loop in seq_len(N)) {
random_sign <- sample(c(-1, 1), size = nrow(score_diff), replace = TRUE)
statistic[loop, ] <- score_diff %>% summarise(se = mean(random_sign * se),
ds = mean(random_sign * ds))
}
p_values <-
statistic %>%summarise(se = mean(se > statistic0$se),
ds = mean(ds > statistic0$ds))
# Estimates:
return(p_values)
}
#' arch_loglike
#'
#' @description compute the combined likelihood
#'
#' @param data is a data frame with columns N, phi
#' @param y is the vector of observations
#' @return
arch_loglike <- function(data, y){
N <- data$N
phi <- data$phi
log_likelihood <- -lgamma(y[1] + 1) - lgamma(y[2] + 1) - lgamma(N - y[1] + 1) - lgamma(N - y[2] + 1) +
2 * lgamma(N + 1) + (y[1] + y[2]) * log(phi) + (2 * N - y[1] - y[2]) * log(1 - phi)
return(log_likelihood)
}
#' estimate
#'
#' @description
#' @param y the observations
#' @param xi the prior for n
#' @param a prior for phi
#' @param b prior for phi
#' @param K the number of samples
#' @return
estimate <- function(y, xi, a, b, K){
# Sample from the prior distributions
N_samples <- rgeom(K, xi)
phi_samples <- rbeta(K, a, b)
# Calculate the unnormalized posterior probabilities
data <- data.frame(N = N_samples, phi_samples)
log_posterior <- arch_loglike(data, y)
posterior_unnormalized <- exp(log_posterior)
# Calculate the normalization constant
normalization_constant <- sum(posterior_unnormalized)
# Calculate posterior probability p(N|y)
p_N_given_y <- posterior_unnormalized / normalization_constant
# Calculate expected value of N given y
E_N_given_y <- sum(N_samples * p_N_given_y)
# Calculate expected value of phi given y
E_phi_given_y <- sum(phi_samples * p_N_given_y)
# Calculate Monte Carlo estimate of p(y)
p_y <- normalization_constant / K
# Return results
results <- list(
p_y = p_y,
E_N_given_y = E_N_given_y,
E_phi_given_y = E_phi_given_y
)
return(results)
}</code></pre>
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