Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables
Authors: Guangyao Zhou
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The superior performances of M-HMC over existing methods are demonstrated with numerical experiments on Gaussian mixture models (GMMs), variable selection in Bayesian logistic regression (BLR), and correlated topic models (CTMs). |
| Researcher Affiliation | Industry | Guangyao Zhou Vicarious AI Union City, CA 94587, USA stannis@vicarious.com |
| Pseudocode | Yes | Algorithm 1 M-HMC with Laplace momentum |
| Open Source Code | Yes | Code available at https://github.com/Stannis Zhou/mixed_hmc |
| Open Datasets | Yes | We use the Associated Press (AP) dataset [15], which consists of 2246 documents. |
| Dataset Splits | No | The paper specifies burn-in and actual sample counts for MCMC chains but does not provide explicit training, validation, or test dataset splits in the traditional machine learning sense for model training. |
| Hardware Specification | No | The paper mentions that implementations rely on JAX but does not specify any particular CPU or GPU models, or other hardware details used for running experiments. |
| Software Dependencies | No | The paper mentions software like JAX, NUMBA, pypolyagamma, Numpyro, and arviz, but it does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For each sampler, we draw 104 burn-in and 104 actual samples in 192 independent chains. For each sampler, we use 192 independent chains, each with 1000 burn-in and 2000 actual samples. For M-HMC, we inspect short trial runs on a separate document, and fix T, n D for all 20 picked documents and set L = 80 Nd for document d. |