Kevin P Murphy

Replace icons with text tags that can be searched

2

The icons for "Multi-view Images", "Volumetric", etc are cute, but it is (1) impossible to use them for searching, and (2) hard to remember what they mean. Maybe use text...

please specify the version (date) of the book when you post errors. Also please specify if the page number is the pdf viewer page number, or the latex (print) page...

replace jax.ops.index_update with jax.numpy.at

1

jax.ops.index_update has been removed (see https://github.com/google/jax/commit/f51a05a889f2fcb19946352b9d65f2b6c49fec4a) which breaks all the bandit code. Please use [jnp.at()](https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.ndarray.at.html) instead.

viterbi is broken?

4

Old figure new one is different https://github.com/probml/JSL/blob/main/jsl/demos/hmm_casino.py

add unit tests for Kalman filter library

13

Verify that the JSL kalman filter / smoother code returns the same marginal means, covariances and log marginal likelihood as when using [tfd.LinearGaussianModel](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/LinearGaussianStateSpaceModel), similar to the 1d test used in...

In https://github.com/probml/JSL/blob/main/jsl/demos/hmm_lillypad.py we use ``` hmm = HMM(trans_dist=distrax.Categorical(probs=A), init_dist=distrax.Categorical(probs=initial_probs), obs_dist=distrax.as_distribution( tfp.substrates.jax.distributions.MultivariateNormalFullCovariance(loc=mu_collection, covariance_matrix=cov_collection))) ``` but it fails when I switch to ``` hmm = HMM(trans_dist=distrax.Categorical(probs=A), init_dist=distrax.Categorical(probs=initial_probs), obs_dist=distrax.MultivariateNormalFullCovariance( loc=mu_collection, covariance_matrix=cov_collection)) ``` Why?

I added back a commented out paragraph on Cramer-Rao lower bound to sec 4.7.6.2 (Variance of an estimator). Context: https://news.ycombinator.com/item?id=30552869

add footnote discucssing some issues with CIFAR-100 dataset (based on https://twitter.com/rasbt/status/1554458573396234241?s=27&t=eZTRpRz4X7g__rl3d7vNqA)

To solve https://github.com/probml/pml-book/issues/440 Tweak https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/07/gaussEvec.ipynb The exponents should be lambda^{-1/2}. I tried changing the text but the figure no longer renders properly (u_1, u_2 axis broken)

update logreg laplace to use bijax

3

https://github.com/probml/pyprobml/blob/master/notebooks/book1/10/logreg_laplace_demo.ipynb