Spherical Hamiltonian Monte Carlo for Constrained Target Distributions
Authors: Shiwei Lan, Bo Zhou, Babak Shahbaba
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our proposed methods, Spherical HMC, by comparing its efficiency to that of Random Walk Metropolis (RWM) and Wall HMC using simulated and real data. To this end, we define efficiency in terms of time-normalized effective sample size (ESS). Given B MCMC samples for each parameter, ESS = B[1+ 2ΣK k=1γ(k)] 1, where ΣK k=1γ(k) is the sum of K monotone sample autocorrelations (Geyer, 1992). We use the minimum ESS normalized by the CPU time, s (in seconds), as the overall measure of efficiency: min(ESS)/s. |
| Researcher Affiliation | Academia | Shiwei Lan SLAN@UCI.EDU Department of Statistics, University of California, Irvine, CA 92697, USA. Bo Zhou BZHOU1@UCI.EDU Department of Statistics, University of California, Irvine, CA 92697, USA. Babak Shahbaba BABAKS@UCI.EDU Department of Statistics, University of California, Irvine, CA 92697, USA. |
| Pseudocode | Yes | Algorithm 1 Spherical HMC |
| Open Source Code | Yes | All computer codes are available online at http://www.ics.uci.edu/ slan/Sph HMC. |
| Open Datasets | Yes | We evaluate our method based on the diabetes data set (N=442, D=10) discussed in (Park & Casella, 2008). |
| Dataset Splits | No | The paper mentions obtaining MCMC samples and discarding initial ones but does not specify training, validation, or test dataset splits or cross-validation methods. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or cloud computing resources used for experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers, nor does it list any versioned libraries or solvers used for its implementation or experiments. |
| Experiment Setup | Yes | For Wall HMC and Spherical HMC, we fix the number of leapfrog steps to 10 and set the trajectory length such that they both have comparable acceptance rates around 70%. |