- Follow the contributing guidelines and specific instructions given over here.
Total Notebooks | Latexified | Jaxified |
---|---|---|
203 | 82 | 70 |
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
student_laplace_pdf_plot.ipynb | 2.1 | ![]() |
❌ | ❌ |
sub_super_gauss_plot.ipynb | 2.2 | ![]() |
✅ | ✅ |
pareto_dist_plot.ipynb | 2.3 | ![]() |
✅ | ✅ |
zipfs_law_plot.ipynb | 2.4 | ![]() |
✅ | ✅ |
gauss_plot_2d.ipynb | 2.5 | ![]() |
❌ | ❌ |
sensor_fusion_2d.ipynb | 2.7 | ![]() |
❌ | ❌ |
wishart_plot.ipynb | 2.8 | ![]() |
✅ | ✅ |
wishart_plot.ipynb | 2.9 | ![]() |
✅ | ✅ |
dirichlet_3d_triangle_plot.ipynb | 2.1 | ![]() |
❌ | ❌ |
dirichlet_3d_spiky_plot.ipynb | 2.1 | ![]() |
❌ | ❌ |
dirichlet_samples_plot.ipynb | 2.11 | ![]() |
❌ | ❌ |
bayes_change_of_var.ipynb | 2.13 | ![]() |
❌ | ❌ |
ecdf_sample.ipynb | 2.14 | ![]() |
✅ | ✅ |
ngram_character_demo.ipynb | 2.17 | ![]() |
❌ | ❌ |
bigram_hinton_diagram.ipynb | 2.18 | ![]() |
❌ | ❌ |
Chapter: 3_Bayesian statistics
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
linreg_post_pred_plot.ipynb | 3.1 | ![]() |
✅ | ✅ |
bimodal_dist_plot.ipynb | 3.2 | ![]() |
❌ | ❌ |
gamma_dist_plot.ipynb | 3.2 | ![]() |
❌ | ❌ |
gauss_infer_1d.ipynb | 3.4 | ![]() |
❌ | ❌ |
gauss_seq_update_sigma_1d.ipynb | 3.5 | ![]() |
✅ | ❌ |
nix_plots.ipynb | 3.6 | ![]() |
❌ | ❌ |
gauss_infer_2d.ipynb | 3.7 | ![]() |
❌ | ❌ |
lkj_1d.ipynb | 3.9 | ![]() |
✅ | ✅ |
maxent_priors.ipynb | 3.1 | ![]() |
❌ | ❌ |
jeffreys_prior_binomial.ipynb | 3.11 | ![]() |
❌ | ❌ |
hbayes_binom_rats.ipynb | 3.13 | ![]() |
❌ | ❌ |
schools8.ipynb | 3.14 | ![]() |
❌ | ❌ |
schools8.ipynb | 3.15 | ![]() |
❌ | ❌ |
schools8.ipynb | 3.16 | ![]() |
❌ | ❌ |
eb_binom.ipynb | 3.18 | ![]() |
✅ | ❌ |
newcomb_plugin_demo.ipynb | 3.21 | ![]() |
✅ | ❌ |
linreg_divorce_ppc.ipynb | 3.22 | ![]() |
❌ | ✅ |
Chapter: 4_Probabilistic graphical models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
student_pgm.ipynb | 4.2 | ![]() |
❌ | ❌ |
berksons_gaussian.ipynb | 4.6 | ![]() |
❌ | ❌ |
student_pgm.ipynb | 4.7 | ![]() |
❌ | ❌ |
gibbs_demo_ising.ipynb | 4.16 | ![]() |
✅ | ✅ |
gibbs_demo_potts.ipynb | 4.17 | ![]() |
❌ | ✅ |
hopfield_demo.ipynb | 4.18 | ![]() |
❌ | ❌ |
rbm_contrastive_divergence.ipynb | 4.2 | ![]() |
✅ | ✅ |
ising_image_denoise_demo.ipynb | 4.26 | ![]() |
❌ | ❌ |
Chapter: 5_Information theory
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
bernoulli_entropy_fig.ipynb | 5.3 | ![]() |
❌ | ❌ |
newsgroups_visualize.ipynb | 5.7 | ![]() |
❌ | ❌ |
relevance_network_newsgroup_demo.ipynb | 5.8 | ![]() |
❌ | ❌ |
error_correcting_code_demo.ipynb | 5.1 | ![]() |
❌ | ❌ |
vib_demo_2021.ipynb | 5.12 | ![]() |
❌ | ❌ |
Chapter: 6_Optimization
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
nat_grad_demo.ipynb | 6.3 | ![]() |
✅ | ❌ |
em_log_likelihood_max.ipynb | 6.6 | ![]() |
❌ | ❌ |
gauss_imputation_em_demo.ipynb | 6.7 | ![]() |
❌ | ❌ |
var_em_bound.ipynb | 6.8 | ![]() |
✅ | ✅ |
simulated_annealing_2d_demo.ipynb | 6.13 | ![]() |
✅ | ✅ |
simulated_annealing_2d_demo.ipynb | 6.14 | ![]() |
✅ | ✅ |
simulated_annealing_2d_demo.ipynb | 6.15 | ![]() |
✅ | ✅ |
Chapter: 7_Inference algorithms: an overview
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
laplace_approx_beta_binom.ipynb | 7.2 | ![]() |
❌ | ❌ |
advi_beta_binom.ipynb | 7.3 | ![]() |
✅ | ✅ |
hmc_beta_binom.ipynb | 7.4 | ![]() |
✅ | ✅ |
Chapter: 8_Inference for state-space models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
casino_hmm.ipynb | 8.4 | ![]() |
❌ | ❌ |
kf_tracking.ipynb | 8.8 | ![]() |
❌ | ❌ |
discretized_ssm_student.ipynb | 8.9 | ![]() |
✅ | ✅ |
discretized_ssm_student.ipynb | 8.1 | ![]() |
✅ | ✅ |
ekf_vs_ukf.ipynb | 8.13 | ![]() |
❌ | ❌ |
pendulum_1d.ipynb | 8.15 | ![]() |
❌ | ❌ |
ekf_vs_ukf.ipynb | 8.17 | ![]() |
❌ | ❌ |
adf_logistic_regression_demo.ipynb | 8.22 | ![]() |
❌ | ❌ |
adf_logistic_regression_demo.ipynb | 8.23 | ![]() |
❌ | ❌ |
Chapter: 9_Inference for graphical models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
gauss-bp-1d-line.ipynb | 9.5 | ![]() |
❌ | ❌ |
Chapter: 10_Variational inference
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
ising_image_denoise_demo.ipynb | 10.3 | ![]() |
❌ | ❌ |
unigauss_vb_demo.ipynb | 10.5 | ![]() |
✅ | ✅ |
variational_mixture_gaussians_demo.ipynb | 10.7 | ![]() |
❌ | ❌ |
variational_mixture_gaussians_demo.ipynb | 10.8 | ![]() |
❌ | ❌ |
variational_mixture_gaussians_demo.ipynb | 10.9 | ![]() |
❌ | ❌ |
vb_gmm.ipynb | 10.13 | ![]() |
❌ | ❌ |
svi_gmm_demo_2d.ipynb | 10.15 | ![]() |
✅ | ❌ |
kl_pq_gauss.ipynb | 10.18 | ![]() |
❌ | ❌ |
Chapter: 11_Monte Carlo inference
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
mc_estimate_pi.ipynb | 11.1 | ![]() |
✅ | ✅ |
mc_accuracy_demo.ipynb | 11.2 | ![]() |
✅ | ✅ |
rejection_sampling_demo.ipynb | 11.4 | ![]() |
✅ | ❌ |
ars_envelope.ipynb | 11.5 | ![]() |
✅ | ❌ |
ars_demo.ipynb | 11.5 | ![]() |
✅ | ❌ |
Chapter: 12_Markov Chain Monte Carlo inference
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
mcmc_gmm_demo.ipynb | 12.1 | ![]() |
✅ | ✅ |
ising_image_denoise_demo.ipynb | 12.3 | ![]() |
❌ | ❌ |
mcmc_gmm_demo.ipynb | 12.4 | ![]() |
✅ | ✅ |
gibbs_gauss_demo.ipynb | 12.5 | ![]() |
✅ | ❌ |
slice_sampling_demo_1d.ipynb | 12.8 | ![]() |
✅ | ❌ |
slice_sampling_demo_2d.ipynb | 12.9 | ![]() |
❌ | ❌ |
random_walk_integers.ipynb | 12.12 | ![]() |
✅ | ✅ |
mcmc_traceplots_unigauss.ipynb | 12.14 | ![]() |
✅ | ✅ |
mcmc_traceplots_unigauss.ipynb | 12.15 | ![]() |
✅ | ✅ |
mcmc_traceplots_unigauss.ipynb | 12.16 | ![]() |
✅ | ✅ |
mcmc_traceplots_unigauss.ipynb | 12.17 | ![]() |
✅ | ✅ |
rhat_slow_mixing_chains.ipynb | 12.18 | ![]() |
✅ | ✅ |
mcmc_gmm_demo.ipynb | 12.19 | ![]() |
✅ | ✅ |
neals_funnel.ipynb | 12.2 | ![]() |
✅ | ✅ |
Chapter: 13_Sequential Monte Carlo inference
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
bootstrap_filter.ipynb | 13.1 | ![]() |
❌ | ❌ |
sis_vs_smc.ipynb | 13.2 | ![]() |
❌ | ❌ |
sis_vs_smc.ipynb | 13.3 | ![]() |
❌ | ❌ |
pf_guided_neural_decoding.ipynb | 13.5 | ![]() |
❌ | ❌ |
rbpf_maneuver.ipynb | 13.6 | ![]() |
❌ | ❌ |
bootstrap_filter_maneuver.ipynb | 13.6 | ![]() |
❌ | ❌ |
rbpf_maneuver_demo.ipynb | 13.7 | ![]() |
❌ | ❌ |
rbpf_maneuver_demo.ipynb | 13.8 | ![]() |
❌ | ❌ |
smc_tempered_1d_bimodal.ipynb | 13.11 | ![]() |
✅ | ✅ |
smc_tempered_1d_bimodal.ipynb | 13.12 | ![]() |
✅ | ✅ |
smc_ibis_1d.ipynb | 13.13 | ![]() |
✅ | ❌ |
Chapter: 14_Predictive models: an overview
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
softmax_plot.ipynb | 14.3 | ![]() |
❌ | ❌ |
Chapter: 15_Generalized linear models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
linreg_height_weight.ipynb | 15.1 | ![]() |
✅ | ✅ |
linreg_height_weight.ipynb | 15.2 | ![]() |
✅ | ✅ |
logreg_prior_offset.ipynb | 15.5 | ![]() |
✅ | ❌ |
logreg_prior.ipynb | 15.5 | ![]() |
✅ | ❌ |
logreg_laplace_demo.ipynb | 15.6 | ![]() |
❌ | ❌ |
logreg_laplace_demo.ipynb | 15.7 | ![]() |
❌ | ❌ |
logreg_iris_bayes_2d.ipynb | 15.8 | ![]() |
✅ | ✅ |
probit_plot.ipynb | 15.9 | ![]() |
✅ | ❌ |
probit_reg_demo.ipynb | 15.1 | ![]() |
✅ | ✅ |
linreg_hierarchical_non_centered.ipynb | 15.12 | ![]() |
❌ | ❌ |
linreg_hierarchical_non_centered.ipynb | 15.13 | ![]() |
❌ | ❌ |
linreg_hierarchical_non_centered.ipynb | 15.14 | ![]() |
❌ | ❌ |
Chapter: 16_Deep neural networks
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
activation_fun_deriv.ipynb | 16.2 | ![]() |
❌ | ❌ |
lecun1989.ipynb | 16.11 | ![]() |
❌ | ✅ |
Chapter: 17_Bayesian neural networks
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
mlp_priors_demo.ipynb | 17.1 | ![]() |
✅ | ✅ |
bnn_mlp_2d_hmc.ipynb | 17.3 | ![]() |
✅ | ✅ |
randomized_priors.ipynb | 17.6 | ![]() |
✅ | ✅ |
ekf_mlp.ipynb | 17.21 | ![]() |
❌ | ❌ |
bnn_hierarchical.ipynb | 17.22 | ![]() |
✅ | ✅ |
hbayes_figures2.ipynb | 17.23 | ![]() |
❌ | ❌ |
bnn_hierarchical.ipynb | 17.24 | ![]() |
✅ | ✅ |
bnn_hierarchical.ipynb | 17.25 | ![]() |
✅ | ✅ |
Chapter: 18_Gaussian processes
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
gpr_demo_ard.ipynb | 18.2 | ![]() |
✅ | ✅ |
gp_kernel_plot.ipynb | 18.3 | ![]() |
❌ | ❌ |
gp_kernel_plot.ipynb | 18.4 | ![]() |
❌ | ❌ |
combining_kernels_by_multiplication.ipynb | 18.5 | ![]() |
✅ | ✅ |
combining_kernels_by_summation.ipynb | 18.6 | ![]() |
✅ | ✅ |
gpr_demo_noise_free.ipynb | 18.7 | ![]() |
✅ | ✅ |
krr_vs_gpr.ipynb | 18.8 | ![]() |
✅ | ✅ |
gpc_demo_2d.ipynb | 18.9 | ![]() |
❌ | ❌ |
gp_poisson_1d.ipynb | 18.1 | ![]() |
✅ | ✅ |
gp_spatial_demo.ipynb | 18.11 | ![]() |
❌ | ❌ |
gpr_demo_change_hparams.ipynb | 18.15 | ![]() |
✅ | ✅ |
gpr_demo_marglik.ipynb | 18.16 | ![]() |
✅ | ✅ |
gp_kernel_opt.ipynb | 18.18 | ![]() |
✅ | ✅ |
gp_spectral_mixture.ipynb | 18.23 | ![]() |
✅ | ✅ |
gp_deep_kernel_learning.ipynb | 18.26 | ![]() |
✅ | ✅ |
deepgp_stepdata.ipynb | 18.32 | ![]() |
✅ | ✅ |
gp_mauna_loa.ipynb | 18.34 | ![]() |
❌ | ❌ |
Chapter: 19_Beyond the iid assumption
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
bnn_mnist_sgld.ipynb | 19.8 | ![]() |
❌ | ❌ |
bnn_mnist_sgld.ipynb | 19.9 | ![]() |
❌ | ❌ |
Chapter: 20_Generative models: an overview
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
parzen_window_demo2.ipynb | 20.4 | ![]() |
❌ | ❌ |
vae_compare_results.ipynb | 20.7 | ![]() |
❌ | ❌ |
vae_celebA_lightning.ipynb | 20.8 | ![]() |
❌ | ❌ |
Chapter: 21_Variational autoencoders
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
vae_compare_results.ipynb | 21.3 | ![]() |
❌ | ❌ |
vae_compare_results.ipynb | 21.4 | ![]() |
❌ | ❌ |
vae_latent_space.ipynb | 21.7 | ![]() |
❌ | ❌ |
vdvae_demo_cifar.ipynb | 21.18 | ![]() |
❌ | ✅ |
quantized_autoencoder_mnist.ipynb | 21.21 | ![]() |
❌ | ❌ |
Chapter: 23_Normalizing Flows
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
flow_2d_mlp.ipynb | 23.1 | ![]() |
✅ | ✅ |
flow_spline_mnist.ipynb | 23.4 | ![]() |
❌ | ✅ |
two_moons_normalizing_flow.ipynb | 23.8 | ![]() |
❌ | ❌ |
Chapter: 24_Energy-based models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
score_matching_swiss_roll.ipynb | 24.3 | ![]() |
❌ | ✅ |
Chapter: 25_Diffusion models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
vdm_2d.ipynb | 25.2 | ![]() |
❌ | ✅ |
Chapter: 26_Generative adversarial networks
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
genmo_types_implicit_explicit.ipynb | 26.1 | ![]() |
❌ | ❌ |
ipm_divergences.ipynb | 26.4 | ![]() |
❌ | ✅ |
ipm_divergences.ipynb | 26.5 | ![]() |
❌ | ✅ |
gan_loss_types.ipynb | 26.6 | ![]() |
✅ | ✅ |
gan_mixture_of_gaussians.ipynb | 26.7 | ![]() |
✅ | ✅ |
dirac_gan.ipynb | 26.8 | ![]() |
❌ | ❌ |
dirac_gan.ipynb | 26.9 | ![]() |
❌ | ❌ |
Chapter: 28_Latent factor models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
gmm_plot_demo.ipynb | 28.1 | ![]() |
❌ | ❌ |
gmm_2d.ipynb | 28.2 | ![]() |
❌ | ❌ |
mix_bernoulli_em_mnist.ipynb | 28.3 | ![]() |
❌ | ❌ |
mix_ppca_demo.ipynb | 28.8 | ![]() |
❌ | ❌ |
mix_ppca_celebA.ipynb | 28.11 | ![]() |
❌ | ❌ |
mix_ppca_celebA.ipynb | 28.13 | ![]() |
❌ | ❌ |
mix_ppca_celebA.ipynb | 28.14 | ![]() |
❌ | ❌ |
binary_fa_demo.ipynb | 28.18 | ![]() |
❌ | ❌ |
gplvm_mocap.ipynb | 28.19 | ![]() |
❌ | ❌ |
ica_demo.ipynb | 28.31 | ![]() |
✅ | ❌ |
ica_demo_uniform.ipynb | 28.32 | ![]() |
✅ | ❌ |
sparse_dict_demo.ipynb | 28.33 | ![]() |
❌ | ❌ |
Chapter: 29_State-space models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
hmm_bernoulli.ipynb | 29.2 | ![]() |
❌ | ❌ |
hmm_gaussian_2d.ipynb | 29.3 | ![]() |
❌ | ❌ |
hmm_ar.ipynb | 29.4 | ![]() |
❌ | ❌ |
hmm_ar.ipynb | 29.5 | ![]() |
❌ | ❌ |
hmm_poisson_changepoint.ipynb | 29.6 | ![]() |
❌ | ❌ |
hmm_poisson_changepoint.ipynb | 29.7 | ![]() |
❌ | ❌ |
hmm_poisson_changepoint.ipynb | 29.8 | ![]() |
❌ | ❌ |
hmm_casino_training.ipynb | 29.13 | ![]() |
❌ | ❌ |
hmm_casino_training.ipynb | 29.14 | ![]() |
❌ | ❌ |
hmm_self_loop_dist.ipynb | 29.15 | ![]() |
✅ | ❌ |
changepoint_detection.ipynb | 29.22 | ![]() |
✅ | ✅ |
kf_tracking.ipynb | 29.23 | ![]() |
❌ | ❌ |
kf_linreg.ipynb | 29.24 | ![]() |
❌ | ❌ |
kf_parallel.ipynb | 29.26 | ![]() |
❌ | ❌ |
poisson_lds_example.ipynb | 29.3 | ![]() |
❌ | ❌ |
poisson_lds_example.ipynb | 29.31 | ![]() |
❌ | ❌ |
sts.ipynb | 29.33 | ![]() |
✅ | ❌ |
sts.ipynb | 29.34 | ![]() |
✅ | ❌ |
sts.ipynb | 29.35 | ![]() |
✅ | ❌ |
sts.ipynb | 29.36 | ![]() |
✅ | ❌ |
causal_impact.ipynb | 29.41 | ![]() |
❌ | ❌ |
Chapter: 30_Graph learning
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
ggm_lasso_demo.ipynb | 30.8 | ![]() |
❌ | ❌ |
Chapter: 31_Non-parametric Bayesian models
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
stick_breaking_demo.ipynb | 31.3 | ![]() |
❌ | ❌ |
dp_mixgauss_sample.ipynb | 31.4 | ![]() |
✅ | ✅ |
Chapter: 34_Decision making under uncertainty
nb_name | fig_no | workflow | latexify | jaxify |
---|---|---|---|---|
ab_test_demo.ipynb | 34.4 | ![]() |
✅ | ❌ |
thompson_sampling_linear_gaussian.ipynb | 34.8 | ![]() |
❌ | ✅ |