Merge branch 'main' into mle_fixes

mmcky · web-flow · commit 2512bcfd7156 · 2025-09-01T08:17:43.000+10:00
diff --git a/.github/workflows/cache.yml b/.github/workflows/cache.yml
@@ -17,7 +17,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -15,7 +15,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
@@ -16,7 +16,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch
diff --git a/environment.yml b/environment.yml
@@ -2,17 +2,18 @@ name: quantecon
 channels:
   - default
 dependencies:
-  - python=3.12
-  - anaconda=2024.10
+  - python=3.13
+  - anaconda=2025.06
   - pip
   - pip:
-    - jupyter-book==1.0.3
-    - quantecon-book-theme==0.7.6
-    - sphinx-tojupyter==0.3.0
+    - jupyter-book==1.0.4post1
+    - quantecon-book-theme==0.9.0
+    - sphinx-tojupyter==0.3.1
     - sphinxext-rediraffe==0.2.7
-    - sphinx-reredirects==0.1.4
     - sphinx-exercise==1.0.1
-    - sphinx-proof==0.2.0
-    - ghp-import==1.1.0
-    - sphinxcontrib-youtube==1.3.0 #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
+    - sphinx-proof==0.2.1
+    - sphinxcontrib-youtube==1.4.1
     - sphinx-togglebutton==0.3.2
+    - sphinx-reredirects==0.1.4
+
+
diff --git a/lectures/_config.yml b/lectures/_config.yml
@@ -98,6 +98,9 @@ sphinx:
       advanced:
         - "https://python-advanced.quantecon.org"
         - null
+      jax:
+        - "https://jax.quantecon.org/"
+        - null
     mathjax3_config:
       tex:
         macros:
diff --git a/lectures/aiyagari.md b/lectures/aiyagari.md
@@ -57,7 +57,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon.markov import DiscreteDP
 from numba import jit
diff --git a/lectures/cake_eating_problem.md b/lectures/cake_eating_problem.md
@@ -41,11 +41,10 @@ In what follows, we require the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 ```
 
-## The Model
+## The model
 
 We consider an infinite time horizon $t=0, 1, 2, 3..$
 
@@ -115,7 +114,7 @@ In this problem, the following terminology is standard:
 * $c_t$ is called the **control variable** or the **action**
 * $\beta$ and $\gamma$ are **parameters**
 
-### Trade-Off
+### Trade-off
 
 The key trade-off in the cake-eating problem is this:
 
@@ -145,14 +144,14 @@ parameters*.
 
 Let's see if this is true.
 
-## The Value Function
+## The value function
 
 The first step of our dynamic programming treatment is to obtain the Bellman
 equation.
 
 The next step is to use it to calculate the solution.
 
-### The Bellman Equation
+### The Bellman equation
 
 To this end, we let $v(x)$ be maximum lifetime utility attainable from
 the current time when $x$ units of cake are left.
@@ -199,7 +198,7 @@ If $c$ is chosen optimally using this trade off strategy, then we obtain maximal
 
 Hence, $v(x)$ equals the right hand side of {eq}`bellman-cep`, as claimed.
 
-### An Analytical Solution
+### An analytical solution
 
 It has been shown that, with $u$ as the CRRA utility function in
 {eq}`crra_utility`, the function
@@ -249,7 +248,7 @@ ax.legend(fontsize=12)
 plt.show()
 ```
 
-## The Optimal Policy
+## The optimal policy
 
 Now that we have the value function, it is straightforward to calculate the
 optimal action at each state.
@@ -309,7 +308,7 @@ ax.legend()
 plt.show()
 ```
 
-## The Euler Equation
+## The Euler equation
 
 In the discussion above we have provided a complete solution to the cake
 eating problem in the case of CRRA utility.
@@ -323,7 +322,7 @@ Euler equation.
 This is because, for more difficult problems, this equation
 provides key insights that are hard to obtain by other methods.
 
-### Statement and Implications
+### Statement and implications
 
 The Euler equation for the present problem can be stated as
 
@@ -376,7 +375,7 @@ see proposition 2.2 of {cite}`ma2020income`.
 The following arguments focus on necessity, explaining why an optimal path or
 policy should satisfy the Euler equation.
 
-### Derivation I: A Perturbation Approach
+### Derivation I: a perturbation approach
 
 Let's write $c$ as a shorthand for consumption path $\{c_t\}_{t=0}^\infty$.
 
@@ -444,7 +443,7 @@ $$
 
 This is just the Euler equation.
 
-### Derivation II: Using the Bellman Equation
+### Derivation II: using the Bellman equation
 
 Another way to derive the Euler equation is to use the Bellman equation {eq}`bellman-cep`.
 
diff --git a/lectures/career.md b/lectures/career.md
@@ -48,7 +48,6 @@ We begin with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from numba import jit, prange
@@ -370,7 +369,8 @@ when the worker follows the optimal policy.
 
 In particular, modulo randomness, reproduce the following figure (where the horizontal axis represents time)
 
-```{figure} /_static/lecture_specific/career/career_solutions_ex1_py.png
+```{image} /_static/lecture_specific/career/career_solutions_ex1_py.png
+:align: center
 ```
 
 ```{hint}
diff --git a/lectures/cass_koopmans_2.md b/lectures/cass_koopmans_2.md
@@ -67,7 +67,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import jit, float64
 from numba.experimental import jitclass
 import numpy as np
diff --git a/lectures/exchangeable.md b/lectures/exchangeable.md
@@ -71,7 +71,6 @@ Let’s start with some imports:
 tags: [hide-output]
 ---
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import jit, vectorize
 from math import gamma
 import scipy.optimize as op
diff --git a/lectures/finite_markov.md b/lectures/finite_markov.md
@@ -53,7 +53,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import quantecon as qe
 import numpy as np
 from mpl_toolkits.mplot3d import Axes3D
@@ -1113,7 +1112,8 @@ is known as [PageRank](https://en.wikipedia.org/wiki/PageRank).
 
 To illustrate the idea, consider the following diagram
 
-```{figure} /_static/lecture_specific/finite_markov/web_graph.png
+```{image} /_static/lecture_specific/finite_markov/web_graph.png
+:align: center
 ```
 
 Imagine that this is a miniature version of the WWW, with
diff --git a/lectures/ifp.md b/lectures/ifp.md
@@ -548,7 +548,8 @@ Let's consider how the interest rate affects consumption.
 
 Reproduce the following figure, which shows (approximately) optimal consumption policies for different interest rates
 
-```{figure} /_static/lecture_specific/ifp/ifp_policies.png
+```{image} /_static/lecture_specific/ifp/ifp_policies.png
+:align: center
 ```
 
 * Other than `r`, all parameters are at their default values.
diff --git a/lectures/inventory_dynamics.md b/lectures/inventory_dynamics.md
@@ -48,7 +48,6 @@ Let's start with some imports
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit, float64, prange
 from numba.experimental import jitclass
diff --git a/lectures/kalman.md b/lectures/kalman.md
@@ -565,7 +565,8 @@ In the simulation, take $\theta = 10$, $\hat x_0 = 8$ and $\Sigma_0 = 1$.
 
 Your figure should -- modulo randomness -- look something like this
 
-```{figure} /_static/lecture_specific/kalman/kl_ex1_fig.png
+```{image} /_static/lecture_specific/kalman/kl_ex1_fig.png
+:align: center
 ```
 
 ```{exercise-end}
@@ -629,7 +630,8 @@ Plot $z_t$ against $T$, setting $\epsilon = 0.1$ and $T = 600$.
 
 Your figure should show error erratically declining something like this
 
-```{figure} /_static/lecture_specific/kalman/kl_ex2_fig.png
+```{image} /_static/lecture_specific/kalman/kl_ex2_fig.png
+:align: center
 ```
 
 ```{exercise-end}
@@ -732,7 +734,8 @@ Finally, set $x_0 = (0, 0)$.
 
 You should end up with a figure similar to the following (modulo randomness)
 
-```{figure} /_static/lecture_specific/kalman/kalman_ex3.png
+```{image} /_static/lecture_specific/kalman/kalman_ex3.png
+:align: center
 ```
 
 Observe how, after an initial learning period, the Kalman filter performs quite well, even relative to the competitor who predicts optimally with knowledge of the latent state.
diff --git a/lectures/lake_model.md b/lectures/lake_model.md
@@ -69,7 +69,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon import MarkovChain
 from scipy.stats import norm
diff --git a/lectures/likelihood_bayes.md b/lectures/likelihood_bayes.md
@@ -52,7 +52,6 @@ We'll begin by loading some Python modules.
 :hide-output: false
 
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import vectorize, jit, prange
 from math import gamma
diff --git a/lectures/linear_algebra.md b/lectures/linear_algebra.md
@@ -74,7 +74,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from matplotlib import cm
 from mpl_toolkits.mplot3d import Axes3D
diff --git a/lectures/lln_clt.md b/lectures/lln_clt.md
@@ -52,7 +52,6 @@ We'll need the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import random
 import numpy as np
 from scipy.stats import t, beta, lognorm, expon, gamma, uniform
diff --git a/lectures/lq_inventories.md b/lectures/lq_inventories.md
@@ -204,7 +204,6 @@ its consequences.
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 ```
diff --git a/lectures/lqcontrol.md b/lectures/lqcontrol.md
@@ -71,7 +71,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon import LQ
 ```
diff --git a/lectures/markov_asset.md b/lectures/markov_asset.md
@@ -73,7 +73,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from numpy.linalg import eigvals, solve
diff --git a/lectures/markov_perf.md b/lectures/markov_perf.md
@@ -722,7 +722,8 @@ c1 = c2 = np.array([1, -2, 1])
 e1 = e2 = np.array([10, 10, 3])
 ```
 
-```{figure} /_static/lecture_specific/markov_perf/judd_fig2.png
+```{image} /_static/lecture_specific/markov_perf/judd_fig2.png
+:align: center
 ```
 
 Inventories trend to a common steady state.
@@ -731,7 +732,8 @@ If we increase the depreciation rate to $\delta = 0.05$, then we expect steady s
 
 This is indeed the case, as the next figure shows
 
-```{figure} /_static/lecture_specific/markov_perf/judd_fig1.png
+```{image} /_static/lecture_specific/markov_perf/judd_fig1.png
+:align: center
 ```
 
 In this exercise, reproduce the figure when $\delta = 0.02$.
diff --git a/lectures/mccall_model.md b/lectures/mccall_model.md
@@ -61,7 +61,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit, float64
 from numba.experimental import jitclass
diff --git a/lectures/mix_model.md b/lectures/mix_model.md
@@ -124,7 +124,6 @@ As usual, we'll start by importing some Python tools.
 :hide-output: false
 
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import vectorize, jit
 from math import gamma
diff --git a/lectures/mle.md b/lectures/mle.md
@@ -44,6 +44,7 @@ economic factors such as market size and tax rate predict.
 
 We'll require the following imports:
 
+
 ```{code-cell} ipython3
 import jax.numpy as jnp
 import jax
diff --git a/lectures/multi_hyper.md b/lectures/multi_hyper.md
@@ -112,7 +112,6 @@ Let's start with some imports.
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from scipy.special import comb
 from scipy.stats import normaltest
diff --git a/lectures/multivariate_normal.md b/lectures/multivariate_normal.md
@@ -62,7 +62,6 @@ We  use the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit
 import statsmodels.api as sm
diff --git a/lectures/newton_method.md b/lectures/newton_method.md
diff --git a/lectures/ols.md b/lectures/ols.md
diff --git a/lectures/optgrowth.md b/lectures/optgrowth.md
diff --git a/lectures/optgrowth_fast.md b/lectures/optgrowth_fast.md
diff --git a/lectures/pandas_panel.md b/lectures/pandas_panel.md
diff --git a/lectures/perm_income.md b/lectures/perm_income.md
diff --git a/lectures/rational_expectations.md b/lectures/rational_expectations.md
diff --git a/lectures/samuelson.md b/lectures/samuelson.md
diff --git a/lectures/sir_model.md b/lectures/sir_model.md
diff --git a/lectures/uncertainty_traps.md b/lectures/uncertainty_traps.md
diff --git a/lectures/wealth_dynamics.md b/lectures/wealth_dynamics.md
