DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Authors: Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experimental Results Toy example. The first problem we study is the following Gaussian hierarchical model... In our experiments, we compare full and conditional DR-ABC against SA-ABC and K2-ABC. ... For the performance metric, we calculate the mean square error (MSE) of the parameter of interest on synthetic data. ... Figure 1 describes the performance of our chosen methods across different numbers of particles... |
| Researcher Affiliation | Academia | Jovana Mitrovic MITROVIC@STATS.OX.AC.UK Dino Sejdinovic DINO.SEJDINOVIC@STATS.OX.AC.UK Yee Whye Teh Y.W.TEH@STATS.OX.AC.UK Department of Statistics, University of Oxford |
| Pseudocode | Yes | Algorithm 1 Distribution Regression... Algorithm 2 Conditional Distribution Regression... Algorithm 3 DR-ABC Algorithm |
| Open Source Code | Yes | The code for all presented experiments is available at https://github.com/jovana-mitrovic/dr-abc. |
| Open Datasets | Yes | As an example of an ecological system with a dynamic generative process, we examine the problem of inferring the dynamics of the adult blowfly population as introduced in Wood (2010). |
| Dataset Splits | Yes | The hyperparameters in the two DRABC methods are set via five-fold cross-validation on appropriately defined grids. |
| Hardware Specification | No | The paper discusses computational complexity and efficiency (e.g., 'significantly reduced computational cost'), but it does not specify any hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper refers to code availability on GitHub but does not explicitly list any software dependencies with specific version numbers (e.g., Python version, specific libraries, or frameworks). |
| Experiment Setup | Yes | The hyperparameters in the two DRABC methods are set via five-fold cross-validation on appropriately defined grids. For the grids of the different kernel bandwidth parameters, we multiply the respective median heuristics (Reddi et al., 2014) with a set of ten equally spaced points between 10 4 and 1000. For λ and ϵ, we choose the grids by exponentiating 10 to the powers given by ten equally spaced points between 4 and 1. ... We use either 100 or 200 particles in (conditional) distribution regression. |