shell: bash -l {0}

run: |

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128

pip install pyro-ppl

pip install --upgrade "jax[cuda12-local]"

pip install numpyro pyro-ppl

python scripts/test-jax-install.py

execution-checks:

runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=ubuntu24-gpu-x64/disk=large"

container:

image: docker://us-docker.pkg.dev/colab-images/public/runtime

image: docker://us-docker.pkg.dev/colab-images/public/runtime:latest

options: --gpus all

steps:

- uses: actions/checkout@v4

- name: Install Build Software

shell: bash -l {0}

run: |

pip install jupyter-book==0.15.1 docutils==0.17.1 quantecon-book-theme==0.7.2 sphinx-tojupyter==0.3.0 sphinxext-rediraffe==0.2.7 sphinx-reredirects sphinx-exercise==0.4.1 sphinxcontrib-youtube==1.1.0 sphinx-togglebutton==0.3.1 arviz==0.13.0 sphinx-proof

pip install jupyter-book==1.0.3 quantecon-book-theme==0.8.2 sphinx-tojupyter==0.3.0 sphinxext-rediraffe==0.2.7 sphinxcontrib-youtube==1.3.0 sphinx-togglebutton==0.3.2 arviz sphinx-proof sphinx-exercise sphinx-reredirects

# Build of HTML (Execution Testing)

- name: Build HTML

shell: bash -l {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-togglebutton==0.3.2

# Docker Requirements

- pytz

```{admonition} GPU

:class: warning

This lecture was built using a machine with the latest CUDA and CUDANN frameworks installed with access to a GPU.

To run this lecture on [Google Colab](https://colab.research.google.com/), click on the "play" icon top right, select Colab, and set the runtime environment to include a GPU.

To run this lecture on your own machine, you need to install the software listed following this notice.

```

# Posterior Distributions for AR(1) Parameters

We'll begin with some Python imports.

```{include} _admonition/gpu.md

```

```{code-cell} ipython3

:tags: [hide-output]

!pip install numpyro jax

```

In addition to what's included in base Anaconda, we need to install the following packages

```{code-cell} ipython3

:tags: [hide-output]

!pip install arviz pymc numpyro jax

!pip install arviz pymc

```

We'll begin with some Python imports.

```{code-cell} ipython3

import arviz as az

# Introduction to Artificial Neural Networks

```{include} _admonition/gpu.md

```

```{code-cell} ipython3

:tags: [skip-execution]

!pip install --upgrade jax

```

In addition to what's included in base Anaconda, we need to install the following packages

```{code-cell} ipython3

:tags: [hide-output]

!pip install --upgrade jax jaxlib kaleido

!pip install kaleido

!conda install -y -c plotly plotly plotly-orca retrying

```

# Non-Conjugate Priors

```{include} _admonition/gpu.md

```

```{code-cell} ipython3

:tags: [hide-output]

!pip install numpyro pyro-ppl torch jax

```

This lecture is a sequel to the {doc}`quantecon lecture <prob_meaning>`.

That lecture offers a Bayesian interpretation of probability in a setting in which the likelihood function and the prior distribution

As usual, we begin by importing some Python code.

```{code-cell} ipython3

:tags: [hide-output]

# install dependencies

!pip install numpyro pyro-ppl torch jax

```

```{code-cell} ipython3

import numpy as np

import seaborn as sns

(likelihood-ratio-process)=

# Incorrect Models

In addition to what's in Anaconda, this lecture will need the following libraries:

```{include} _admonition/gpu.md

```

```{code-cell} ipython

---

tags: [hide-output]

---

:tags: [hide-output]

!pip install numpyro jax

```