Applying Multi-objective Bayesian optimization to Likelihood-Free inference

Music City Center 

Scientific statistical models are often defined by generative processes for simulating synthetic data, but many, such as sequential sampling models (SSMs) used in psychology and consumer behavior, involve intractable likelihoods. Likelihood-free inference (LFI) methods address this challenge, enabling Bayesian parameter inference for such models. We propose to apply Multi-objective Bayesian Optimization (MOBO) to LFI for estimation of parameters using multi-source data, such as SSMs parameters using response times and choice outcomes. This approach models discrepancies for each data source separately, using MOBO to efficiently approximate the joint likelihood. This multivariate approach also identifies conflicting information from different data sources and provides insights on their different importance in estimation of individual parameters. We demonstrate the advantages of MOBO over single-discrepancy methods through a synthetic data example and a real-world application evaluating ride-hailing drivers' preferences for electric vehicle rentals in Singapore. While focused on SSMs, our method generalizes to likelihood-free calibration for other multi-source models.

Likelihood-Free Inference

Sequential Sampling Models

Multi-objective Bayesian Optimization 

Section on Bayesian Statistical Science