Calibrated Approximate Bayesian Inference
Authors: Hanwen Xing, Geoff Nicholls, Jeong Lee
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the algorithms to calibrate a pseudo-likelihood approximation. In Section 5, we calibrate the approximate posterior of a random partition in a hierarchical model with a Dirichlet Process prior on the distribution of random effects. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Oxford, UK 2Department of Statistics, University of Auckland, New Zealand. |
| Pseudocode | Yes | Algorithm 1 Estimation of operational coverage c(y)... Algorithm 2 AIS Estimation of operational coverage c(y)... Algorithm 3 Estimation of operational coverage c(y) via regression |
| Open Source Code | No | The paper does not provide concrete access to source code (e.g., a specific repository link or an explicit code release statement) for the methodology described. |
| Open Datasets | Yes | Figure 1 is a 200 by 200 binary image obtained by thresholding a grey-level image of ice floes from Banfield & Raftery (1992). ... Our dataset has the classical format of a complete design, with five categorical variables, including Treatment (N = 12 levels), and four block variables, B1 (with q = 3 levels) B2 (r = 2 levels), B3 (two levels) and B4 (seven levels) so that we have n = 1008 observations, y = (y1, . . . , yn). |
| Dataset Splits | Yes | We simulate M = 810 pairs {c(i), y(i)}M i=1 of training data following Algorithm 3. ... In order to further test the robustness of our method, we partition the full synthetic dataset {c(i), y(i)}M i=1 into four equal-size subsets and fit a Probit BART model using formula M2 to each subset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions the R package "bart Machine" and "lme4" but does not specify their version numbers, nor does it list versions for other ancillary software or libraries. |
| Experiment Setup | Yes | We initialise the AIS sampler with M = 1000 samples {phi_i, y_i}M i=1 with phi_i pi(phi|yobs) and y_i p_F(y_i|phi_i), the true likelihood. We set the number of AIS iterations NAIS = 60 with cooling schedule beta_j = 1.05j for j = 1, . . . , NAIS, gamma_j = 0.02j for j = 1, . . . 50 and gamma_j = 1 for j = 51, . . . , NAIS at the jth iteration. |