Understanding MCMC Dynamics as Flows on the Wasserstein Space
Chang Liu, Jingwei Zhuo, and Jun Zhu
For the synthetic experiment: Directly open "synth_run.ipynb" in a jupyter notebook.
For the Latent Dirichlet Allocation experiment: First run
"python lda_build.py build_ext --inplace"
to compile the Cython code, then run
"python lda_run.py ./lda_sett_icml/[a specific settings file]"
to conduct experiment under the specified settings. The ICML dataset can be downloaded from
Codes are developed based on the codes of "Stochastic Gradient Riemannian Langevin Dynamics for Latent Dirichlet Allocation" (Patterson and Teh, 2013).
For the Bayesian neural network experiment: Directly edit the file "bnn_tq_run.py" to make a setting, and run
to conduct experiment under the specified settings. Experiment setup follows the one of "Stochastic Gradient Hamiltonian Monte Carlo" (Chen et al., 2014)