dashboard_figures_book2.md

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Book2Dashboard

Total Notebooks	Latexified	Jaxified
203	82	70

Chapter: 2_Probability

nb_name	fig_no	workflow	latexify	jaxify
student_laplace_pdf_plot.ipynb	2.1	, log	❌	❌
sub_super_gauss_plot.ipynb	2.2	, log	✅	✅
pareto_dist_plot.ipynb	2.3	, log	✅	✅
zipfs_law_plot.ipynb	2.4	, log	✅	✅
gauss_plot_2d.ipynb	2.5	, log	❌	❌
sensor_fusion_2d.ipynb	2.7	, log	❌	❌
wishart_plot.ipynb	2.8	, log	✅	✅
wishart_plot.ipynb	2.9	, log	✅	✅
dirichlet_3d_triangle_plot.ipynb	2.1	, log	❌	❌
dirichlet_3d_spiky_plot.ipynb	2.1	, log	❌	❌
dirichlet_samples_plot.ipynb	2.11	, log	❌	❌
bayes_change_of_var.ipynb	2.13	, log	❌	❌
ecdf_sample.ipynb	2.14	, log	✅	✅
ngram_character_demo.ipynb	2.17	, log	❌	❌
bigram_hinton_diagram.ipynb	2.18	, log	❌	❌

Chapter: 3_Bayesian statistics

nb_name	fig_no	workflow	latexify	jaxify
linreg_post_pred_plot.ipynb	3.1	, log	✅	✅
bimodal_dist_plot.ipynb	3.2	, log	❌	❌
gamma_dist_plot.ipynb	3.2	, log	❌	❌
gauss_infer_1d.ipynb	3.4	, log	❌	❌
gauss_seq_update_sigma_1d.ipynb	3.5	, log	✅	❌
nix_plots.ipynb	3.6	, log	❌	❌
gauss_infer_2d.ipynb	3.7	, log	❌	❌
lkj_1d.ipynb	3.9	, log	✅	✅
maxent_priors.ipynb	3.1	, log	❌	❌
jeffreys_prior_binomial.ipynb	3.11	, log	❌	❌
hbayes_binom_rats.ipynb	3.13	, log	❌	❌
schools8.ipynb	3.14	, log	❌	❌
schools8.ipynb	3.15	, log	❌	❌
schools8.ipynb	3.16	, log	❌	❌
eb_binom.ipynb	3.18	, log	✅	❌
newcomb_plugin_demo.ipynb	3.21	, log	✅	❌
linreg_divorce_ppc.ipynb	3.22	, log	❌	✅

Chapter: 4_Probabilistic graphical models

nb_name	fig_no	workflow	latexify	jaxify
student_pgm.ipynb	4.2	, log	❌	❌
berksons_gaussian.ipynb	4.6	, log	❌	❌
student_pgm.ipynb	4.7	, log	❌	❌
gibbs_demo_ising.ipynb	4.16	, log	✅	✅
gibbs_demo_potts.ipynb	4.17	, log	❌	✅
hopfield_demo.ipynb	4.18	, log	❌	❌
rbm_contrastive_divergence.ipynb	4.2	, log	✅	✅
ising_image_denoise_demo.ipynb	4.26	, log	❌	❌

Chapter: 5_Information theory

nb_name	fig_no	workflow	latexify	jaxify
bernoulli_entropy_fig.ipynb	5.3	, log	❌	❌
newsgroups_visualize.ipynb	5.7	, log	❌	❌
relevance_network_newsgroup_demo.ipynb	5.8	, log	❌	❌
error_correcting_code_demo.ipynb	5.1	, log	❌	❌
vib_demo_2021.ipynb	5.12	, log	❌	❌

Chapter: 6_Optimization

nb_name	fig_no	workflow	latexify	jaxify
nat_grad_demo.ipynb	6.3	, log	✅	❌
em_log_likelihood_max.ipynb	6.6	, log	❌	❌
gauss_imputation_em_demo.ipynb	6.7	, log	❌	❌
var_em_bound.ipynb	6.8	, log	✅	✅
simulated_annealing_2d_demo.ipynb	6.13	, log	✅	✅
simulated_annealing_2d_demo.ipynb	6.14	, log	✅	✅
simulated_annealing_2d_demo.ipynb	6.15	, log	✅	✅

Chapter: 7_Inference algorithms: an overview

nb_name	fig_no	workflow	latexify	jaxify
laplace_approx_beta_binom.ipynb	7.2	, log	❌	❌
advi_beta_binom.ipynb	7.3	, log	✅	✅
hmc_beta_binom.ipynb	7.4	, log	✅	✅

Chapter: 8_Inference for state-space models

nb_name	fig_no	workflow	latexify	jaxify
casino_hmm.ipynb	8.4	, log	❌	❌
kf_tracking.ipynb	8.8	, log	❌	❌
discretized_ssm_student.ipynb	8.9	, log	✅	✅
discretized_ssm_student.ipynb	8.1	, log	✅	✅
ekf_vs_ukf.ipynb	8.13	, log	❌	❌
pendulum_1d.ipynb	8.15	, log	❌	❌
ekf_vs_ukf.ipynb	8.17	, log	❌	❌
adf_logistic_regression_demo.ipynb	8.22	, log	❌	❌
adf_logistic_regression_demo.ipynb	8.23	, log	❌	❌

Chapter: 9_Inference for graphical models

nb_name	fig_no	workflow	latexify	jaxify
gauss-bp-1d-line.ipynb	9.5	, log	❌	❌

Chapter: 10_Variational inference

nb_name	fig_no	workflow	latexify	jaxify
ising_image_denoise_demo.ipynb	10.3	, log	❌	❌
unigauss_vb_demo.ipynb	10.5	, log	✅	✅
variational_mixture_gaussians_demo.ipynb	10.7	, log	❌	❌
variational_mixture_gaussians_demo.ipynb	10.8	, log	❌	❌
variational_mixture_gaussians_demo.ipynb	10.9	, log	❌	❌
vb_gmm.ipynb	10.13	, log	❌	❌
svi_gmm_demo_2d.ipynb	10.15	, log	✅	❌
kl_pq_gauss.ipynb	10.18	, log	❌	❌

Chapter: 11_Monte Carlo inference

nb_name	fig_no	workflow	latexify	jaxify
mc_estimate_pi.ipynb	11.1	, log	✅	✅
mc_accuracy_demo.ipynb	11.2	, log	✅	✅
rejection_sampling_demo.ipynb	11.4	, log	✅	❌
ars_envelope.ipynb	11.5	, log	✅	❌
ars_demo.ipynb	11.5	, log	✅	❌

Chapter: 12_Markov Chain Monte Carlo inference

nb_name	fig_no	workflow	latexify	jaxify
mcmc_gmm_demo.ipynb	12.1	, log	✅	✅
ising_image_denoise_demo.ipynb	12.3	, log	❌	❌
mcmc_gmm_demo.ipynb	12.4	, log	✅	✅
gibbs_gauss_demo.ipynb	12.5	, log	✅	❌
slice_sampling_demo_1d.ipynb	12.8	, log	✅	❌
slice_sampling_demo_2d.ipynb	12.9	, log	❌	❌
random_walk_integers.ipynb	12.12	, log	✅	✅
mcmc_traceplots_unigauss.ipynb	12.14	, log	✅	✅
mcmc_traceplots_unigauss.ipynb	12.15	, log	✅	✅
mcmc_traceplots_unigauss.ipynb	12.16	, log	✅	✅
mcmc_traceplots_unigauss.ipynb	12.17	, log	✅	✅
rhat_slow_mixing_chains.ipynb	12.18	, log	✅	✅
mcmc_gmm_demo.ipynb	12.19	, log	✅	✅
neals_funnel.ipynb	12.2	, log	✅	✅

Chapter: 13_Sequential Monte Carlo inference

nb_name	fig_no	workflow	latexify	jaxify
bootstrap_filter.ipynb	13.1	, log	❌	❌
sis_vs_smc.ipynb	13.2	, log	❌	❌
sis_vs_smc.ipynb	13.3	, log	❌	❌
pf_guided_neural_decoding.ipynb	13.5	, log	❌	❌
rbpf_maneuver.ipynb	13.6	, log	❌	❌
bootstrap_filter_maneuver.ipynb	13.6	, log	❌	❌
rbpf_maneuver_demo.ipynb	13.7	, log	❌	❌
rbpf_maneuver_demo.ipynb	13.8	, log	❌	❌
smc_tempered_1d_bimodal.ipynb	13.11	, log	✅	✅
smc_tempered_1d_bimodal.ipynb	13.12	, log	✅	✅
smc_ibis_1d.ipynb	13.13	, log	✅	❌

Chapter: 14_Predictive models: an overview

nb_name	fig_no	workflow	latexify	jaxify
softmax_plot.ipynb	14.3	, log	❌	❌

Chapter: 15_Generalized linear models

nb_name	fig_no	workflow	latexify	jaxify
linreg_height_weight.ipynb	15.1	, log	✅	✅
linreg_height_weight.ipynb	15.2	, log	✅	✅
logreg_prior_offset.ipynb	15.5	, log	✅	❌
logreg_prior.ipynb	15.5	, log	✅	❌
logreg_laplace_demo.ipynb	15.6	, log	❌	❌
logreg_laplace_demo.ipynb	15.7	, log	❌	❌
logreg_iris_bayes_2d.ipynb	15.8	, log	✅	✅
probit_plot.ipynb	15.9	, log	✅	❌
probit_reg_demo.ipynb	15.1	, log	✅	✅
linreg_hierarchical_non_centered.ipynb	15.12	, log	❌	❌
linreg_hierarchical_non_centered.ipynb	15.13	, log	❌	❌
linreg_hierarchical_non_centered.ipynb	15.14	, log	❌	❌

Chapter: 16_Deep neural networks

nb_name	fig_no	workflow	latexify	jaxify
activation_fun_deriv.ipynb	16.2	, log	❌	❌
lecun1989.ipynb	16.11	, log	❌	✅

Chapter: 17_Bayesian neural networks

nb_name	fig_no	workflow	latexify	jaxify
mlp_priors_demo.ipynb	17.1	, log	✅	✅
bnn_mlp_2d_hmc.ipynb	17.3	, log	✅	✅
randomized_priors.ipynb	17.6	, log	✅	✅
ekf_mlp.ipynb	17.21	, log	❌	❌
bnn_hierarchical.ipynb	17.22	, log	✅	✅
hbayes_figures2.ipynb	17.23	, log	❌	❌
bnn_hierarchical.ipynb	17.24	, log	✅	✅
bnn_hierarchical.ipynb	17.25	, log	✅	✅

Chapter: 18_Gaussian processes

nb_name	fig_no	workflow	latexify	jaxify
gpr_demo_ard.ipynb	18.2	, log	✅	✅
gp_kernel_plot.ipynb	18.3	, log	❌	❌
gp_kernel_plot.ipynb	18.4	, log	❌	❌
combining_kernels_by_multiplication.ipynb	18.5	, log	✅	✅
combining_kernels_by_summation.ipynb	18.6	, log	✅	✅
gpr_demo_noise_free.ipynb	18.7	, log	✅	✅
krr_vs_gpr.ipynb	18.8	, log	✅	✅
gpc_demo_2d.ipynb	18.9	, log	❌	❌
gp_poisson_1d.ipynb	18.1	, log	✅	✅
gp_spatial_demo.ipynb	18.11	, log	❌	❌
gpr_demo_change_hparams.ipynb	18.15	, log	✅	✅
gpr_demo_marglik.ipynb	18.16	, log	✅	✅
gp_kernel_opt.ipynb	18.18	, log	✅	✅
gp_spectral_mixture.ipynb	18.23	, log	✅	✅
gp_deep_kernel_learning.ipynb	18.26	, log	✅	✅
deepgp_stepdata.ipynb	18.32	, log	✅	✅
gp_mauna_loa.ipynb	18.34	, log	❌	❌

Chapter: 19_Beyond the iid assumption

nb_name	fig_no	workflow	latexify	jaxify
bnn_mnist_sgld.ipynb	19.8	, log	❌	❌
bnn_mnist_sgld.ipynb	19.9	, log	❌	❌

Chapter: 20_Generative models: an overview

nb_name	fig_no	workflow	latexify	jaxify
parzen_window_demo2.ipynb	20.4	, log	❌	❌
vae_compare_results.ipynb	20.7	, log	❌	❌
vae_celebA_lightning.ipynb	20.8	, log	❌	❌

Chapter: 21_Variational autoencoders

nb_name	fig_no	workflow	latexify	jaxify
vae_compare_results.ipynb	21.3	, log	❌	❌
vae_compare_results.ipynb	21.4	, log	❌	❌
vae_latent_space.ipynb	21.7	, log	❌	❌
vdvae_demo_cifar.ipynb	21.18	, log	❌	✅
quantized_autoencoder_mnist.ipynb	21.21	, log	❌	❌

Chapter: 23_Normalizing Flows

nb_name	fig_no	workflow	latexify	jaxify
flow_2d_mlp.ipynb	23.1	, log	✅	✅
flow_spline_mnist.ipynb	23.4	, log	❌	✅
two_moons_normalizing_flow.ipynb	23.8	, log	❌	❌

Chapter: 24_Energy-based models

nb_name	fig_no	workflow	latexify	jaxify
score_matching_swiss_roll.ipynb	24.3	, log	❌	✅

Chapter: 25_Diffusion models

nb_name	fig_no	workflow	latexify	jaxify
vdm_2d.ipynb	25.2	, log	❌	✅

Chapter: 26_Generative adversarial networks

nb_name	fig_no	workflow	latexify	jaxify
genmo_types_implicit_explicit.ipynb	26.1	, log	❌	❌
ipm_divergences.ipynb	26.4	, log	❌	✅
ipm_divergences.ipynb	26.5	, log	❌	✅
gan_loss_types.ipynb	26.6	, log	✅	✅
gan_mixture_of_gaussians.ipynb	26.7	, log	✅	✅
dirac_gan.ipynb	26.8	, log	❌	❌
dirac_gan.ipynb	26.9	, log	❌	❌

Chapter: 28_Latent factor models

nb_name	fig_no	workflow	latexify	jaxify
gmm_plot_demo.ipynb	28.1	, log	❌	❌
gmm_2d.ipynb	28.2	, log	❌	❌
mix_bernoulli_em_mnist.ipynb	28.3	, log	❌	❌
mix_ppca_demo.ipynb	28.8	, log	❌	❌
mix_ppca_celebA.ipynb	28.11	, log	❌	❌
mix_ppca_celebA.ipynb	28.13	, log	❌	❌
mix_ppca_celebA.ipynb	28.14	, log	❌	❌
binary_fa_demo.ipynb	28.18	, log	❌	❌
gplvm_mocap.ipynb	28.19	, log	❌	❌
ica_demo.ipynb	28.31	, log	✅	❌
ica_demo_uniform.ipynb	28.32	, log	✅	❌
sparse_dict_demo.ipynb	28.33	, log	❌	❌

Chapter: 29_State-space models

nb_name	fig_no	workflow	latexify	jaxify
hmm_bernoulli.ipynb	29.2	, log	❌	❌
hmm_gaussian_2d.ipynb	29.3	, log	❌	❌
hmm_ar.ipynb	29.4	, log	❌	❌
hmm_ar.ipynb	29.5	, log	❌	❌
hmm_poisson_changepoint.ipynb	29.6	, log	❌	❌
hmm_poisson_changepoint.ipynb	29.7	, log	❌	❌
hmm_poisson_changepoint.ipynb	29.8	, log	❌	❌
hmm_casino_training.ipynb	29.13	, log	❌	❌
hmm_casino_training.ipynb	29.14	, log	❌	❌
hmm_self_loop_dist.ipynb	29.15	, log	✅	❌
changepoint_detection.ipynb	29.22	, log	✅	✅
kf_tracking.ipynb	29.23	, log	❌	❌
kf_linreg.ipynb	29.24	, log	❌	❌
kf_parallel.ipynb	29.26	, log	❌	❌
poisson_lds_example.ipynb	29.3	, log	❌	❌
poisson_lds_example.ipynb	29.31	, log	❌	❌
sts.ipynb	29.33	, log	✅	❌
sts.ipynb	29.34	, log	✅	❌
sts.ipynb	29.35	, log	✅	❌
sts.ipynb	29.36	, log	✅	❌
causal_impact.ipynb	29.41	, log	❌	❌

Chapter: 30_Graph learning

nb_name	fig_no	workflow	latexify	jaxify
ggm_lasso_demo.ipynb	30.8	, log	❌	❌

Chapter: 31_Non-parametric Bayesian models

nb_name	fig_no	workflow	latexify	jaxify
stick_breaking_demo.ipynb	31.3	, log	❌	❌
dp_mixgauss_sample.ipynb	31.4	, log	✅	✅

Chapter: 34_Decision making under uncertainty

nb_name	fig_no	workflow	latexify	jaxify
ab_test_demo.ipynb	34.4	, log	✅	❌
thompson_sampling_linear_gaussian.ipynb	34.8	, log	❌	✅