Pseudo-Extended Markov chain Monte Carlo
Authors: Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the pseudo-extended method on three test models. The first two (Sections 4.1 and 4.2) are chosen to show how the pseudo-extended method performs on simulated data when the target is multi-modal. The third example (Section 4.3) is a sparsity-inducing logistic regression model, where multi-modality occurs in the posterior from three real-world datasets. We compare against popular competing algorithms from the literature, including methods discussed in Section 3. |
| Researcher Affiliation | Collaboration | Christopher Nemeth Department of Mathematics and Statistics Lancaster University United Kingdom c.nemeth@lancaster.ac.uk Fredrik Lindsten Department of Computer and Information Science Linköping University Sweden fredrik.lindsten@liu.se Maurizio Filippone Department of Data Science EURECOM France maurizio.filippone@eurecom.fr James Hensman PROWLER.io Cambridge United Kingdom james@prowler.io |
| Pseudocode | Yes | Several numerical integrators are available which preserve the volume and reversibility of the Hamiltonian system (Girolami and Calderhead, 2011), the most popular being the leapfrog integrator which takes L steps, each of size ϵ, though the Hamiltonian dynamics (pseudo-code is given in the Supplementary Material). |
| Open Source Code | Yes | 1https://github.com/chris-nemeth/pseudo-extended-mcmc-code |
| Open Datasets | Yes | We apply this model to three real-world data sets using micro-array data for cancer classification (prostate data results are given in Section E of the Supplementary Material, see Piironen and Vehtari (2017) for further details regarding the data). |
| Dataset Splits | No | The paper mentions 'held-out test data (random 20% of full data)' but does not explicitly provide training/validation/test dataset splits, specifically lacking details on a validation set or its proportion. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | In this paper, we use the No-U-turn sampler (NUTS) introduced by Hoffman and Gelman (2014) as implemented in the STAN (Carpenter et al., 2017) software package to automatically tune L and ϵ. |
| Experiment Setup | Yes | We set db = 28 (d = 27) and let (λ1, λ2) = (6, 2), as these settings have been shown to produce highly multi-modal distributions... Each sampler was run for 50,000 iterations (after burn-in) and the specific tuning details for the temperature ladder of PT and the energy rings for EE are given in Kou et al. (2006). In order to ensure a fair comparison between HMC and pseudo-extended HMC, we run HMC for 10,000 iterations and reduce the number of iterations of the pseudo-extended algorithms (with N = 2 and N = 5) to give equal total computational cost. |