From 1fd52e2ef9b3c1688ba28a45176e9f0777265e68 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 29 Sep 2025 06:27:33 +0000 Subject: [PATCH 1/3] Initial plan From ebf1770071b8433ae103ec538b245e682c79e5a5 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 29 Sep 2025 07:05:51 +0000 Subject: [PATCH 2/3] Fix quotation mark inconsistencies in 5 lecture files Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com> --- lectures/ak2.md | 2 +- lectures/ar1_turningpts.md | 2 +- lectures/exchangeable.md | 2 +- lectures/likelihood_ratio_process.md | 2 +- lectures/likelihood_ratio_process_2.md | 10 +++++----- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/lectures/ak2.md b/lectures/ak2.md index a28398cfc..e1a516725 100644 --- a/lectures/ak2.md +++ b/lectures/ak2.md @@ -226,7 +226,7 @@ r_t & = \alpha K_t^\alpha L_t^{1-\alpha} \end{aligned} $$ (eq:firmfonc) -Output can be consumed either by old people or young people; or sold to young people who use it to augment the capital stock; or sold to the government for uses that do not generate utility for the people in the model (i.e., ``it is thrown into the ocean''). +Output can be consumed either by old people or young people; or sold to young people who use it to augment the capital stock; or sold to the government for uses that do not generate utility for the people in the model (i.e., "it is thrown into the ocean"). The firm thus sells output to old people, young people, and the government. diff --git a/lectures/ar1_turningpts.md b/lectures/ar1_turningpts.md index 3aa55a9df..1dfb0038b 100644 --- a/lectures/ar1_turningpts.md +++ b/lectures/ar1_turningpts.md @@ -276,7 +276,7 @@ $$ This is designed to express the event -- ``after one or two decrease(s), $Y$ will grow for two consecutive quarters'' +- "after one or two decrease(s), $Y$ will grow for two consecutive quarters" Following {cite}`wecker1979predicting`, we can use simulations to calculate probabilities of $P_t$ and $N_t$ for each period $t$. diff --git a/lectures/exchangeable.md b/lectures/exchangeable.md index 4e4d0911e..564cbb1cc 100644 --- a/lectures/exchangeable.md +++ b/lectures/exchangeable.md @@ -262,7 +262,7 @@ So there is something to learn from the past about the future. ## Exchangeability While the sequence $W_0, W_1, \ldots$ is not IID, it can be verified that it is -**exchangeable**, which means that the joint distributions $h(W_0, W_1)$ and $h(W_1, W_0)$ of the ''re-ordered'' sequences +**exchangeable**, which means that the joint distributions $h(W_0, W_1)$ and $h(W_1, W_0)$ of the "re-ordered" sequences satisfy $$ diff --git a/lectures/likelihood_ratio_process.md b/lectures/likelihood_ratio_process.md index fc62a4145..26c315dbb 100644 --- a/lectures/likelihood_ratio_process.md +++ b/lectures/likelihood_ratio_process.md @@ -1725,7 +1725,7 @@ markov_results = analyze_markov_chains(P_f, P_g) Likelihood processes play an important role in Bayesian learning, as described in {doc}`likelihood_bayes` and as applied in {doc}`odu`. -Likelihood ratio processes are central to Lawrence Blume and David Easley's answer to their question ''If you're so smart, why aren't you rich?'' {cite}`blume2006if`, the subject of the lecture{doc}`likelihood_ratio_process_2`. +Likelihood ratio processes are central to Lawrence Blume and David Easley's answer to their question "If you're so smart, why aren't you rich?" {cite}`blume2006if`, the subject of the lecture{doc}`likelihood_ratio_process_2`. Likelihood ratio processes also appear in {doc}`advanced:additive_functionals`, which contains another illustration of the **peculiar property** of likelihood ratio processes described above. diff --git a/lectures/likelihood_ratio_process_2.md b/lectures/likelihood_ratio_process_2.md index ba5c6a603..e187827d4 100644 --- a/lectures/likelihood_ratio_process_2.md +++ b/lectures/likelihood_ratio_process_2.md @@ -29,7 +29,7 @@ kernelspec: ## Overview A likelihood ratio process lies behind Lawrence Blume and David Easley's answer to their question -''If you're so smart, why aren't you rich?'' {cite}`blume2006if`. +"If you're so smart, why aren't you rich?" {cite}`blume2006if`. Blume and Easley constructed formal models to study how differences of opinions about probabilities governing risky income processes would influence outcomes and be reflected in prices of stocks, bonds, and insurance policies that individuals use to share and hedge risks. @@ -148,10 +148,10 @@ f = jit(lambda x: p(x, F_a, F_b)) g = jit(lambda x: p(x, G_a, G_b)) ``` -```{code-cell} ipython3 +`"{code-cell} ipython3 @jit def simulate(a, b, T=50, N=500): - ''' + "' Generate N sets of T observations of the likelihood ratio, return as N x T matrix. ''' @@ -294,7 +294,7 @@ $$ (eq:welfareW) where $\lambda \in [0,1]$ is a Pareto weight that tells how much the planner likes agent $1$ and $1 - \lambda$ is a Pareto weight that tells how much the social planner likes agent $2$. -Setting $\lambda = .5$ expresses ''egalitarian'' social preferences. +Setting $\lambda = .5$ expresses "egalitarian" social preferences. Notice how social welfare criterion {eq}`eq:welfareW` takes into account both agents' preferences as represented by formula {eq}`eq:objectiveagenti`. @@ -369,7 +369,7 @@ values of the likelihood ratio process $l_t(s^t)$: $$l_\infty (s^\infty) = 0; \quad c_\infty^1 = 0$$ -* In the above case, agent 2 is ''smarter'' than agent 1, and agent 1's share of the aggregate endowment converges to zero. +* In the above case, agent 2 is "smarter" than agent 1, and agent 1's share of the aggregate endowment converges to zero. From e87cdc9661d3307ccfb8b322dd4136ee0b2db486 Mon Sep 17 00:00:00 2001 From: Bishmay Barik Date: Mon, 29 Sep 2025 13:33:48 +0530 Subject: [PATCH 3/3] update likelihood_ratio_process_2.md with correct file changes --- lectures/likelihood_ratio_process_2.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lectures/likelihood_ratio_process_2.md b/lectures/likelihood_ratio_process_2.md index e187827d4..6eb9a8c8c 100644 --- a/lectures/likelihood_ratio_process_2.md +++ b/lectures/likelihood_ratio_process_2.md @@ -148,10 +148,10 @@ f = jit(lambda x: p(x, F_a, F_b)) g = jit(lambda x: p(x, G_a, G_b)) ``` -`"{code-cell} ipython3 +```{code-cell} ipython3 @jit def simulate(a, b, T=50, N=500): - "' + ''' Generate N sets of T observations of the likelihood ratio, return as N x T matrix. '''