Finite mixture models do not reliably learn the number of components
Authors: Diana Cai, Trevor Campbell, Tamara Broderick
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate practical consequences of our theory on simulated and real data. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Princeton University 2Department of Statistics, University of British Columbia 3CSAIL, Massachusetts Institute of Technology |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The provided code link (https://github.com/jwmi/Bayesian Mixtures.jl) is for a Julia implementation of a finite mixture model that the authors used for their experiments to illustrate their theory. It is not code that implements the novel theoretical methodology described in the paper. |
| Open Datasets | Yes | single-cell RNA sequencing data from mouse cortex and hippocampus cells (Zeisel et al., 2015) and m RNA expression data from human lung tissue (Bhattacharjee et al., 2001) |
| Dataset Splits | No | The paper describes generating datasets of increasing sizes and using subsets contained within larger ones, and discarding burn-in iterations, but does not specify explicit train/validation/test splits in terms of percentages or counts for data partitioning. |
| Hardware Specification | No | The paper mentions using a "Julia implementation" but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a "Julia implementation of split-merge collapsed Gibbs sampling" and links to "Bayesian Mixtures.jl" but does not specify version numbers for Julia or the package. |
| Experiment Setup | Yes | For all experiments below, we use a finite mixture model with a multivariate Gaussian component family having diagonal covariance matrices and a conjugate prior on each dimension...We set the hyperparameters of the Bayesian finite mixture model as follows: m = 1 2(maxn [ N] Xn+minn [ N] Xn) where N = 10,000, κ = (maxn [ N] Xn minn [ N] Xn) 2, α = 2, r = 0.1, γ = 1, and β Gam(0.2, 10/κ). We ran a total of 100,000 Markov chain Monte Carlo iterations per data set; we discarded the first 10,000 iterations as burn-in. |