Bayesian inference for model selection centres on comparing competing hypotheses by evaluating how well each model explains observed data, accounting for prior beliefs about parameters. The ...

Approximate Bayesian computation (ABC) constitutes a family of likelihood-free methods that have emerged as a cornerstone in statistical inference for complex models where evaluation of the likelihood ...

A Bayesian particle Gibbs framework enables unbiased spike time inference with millisecond resolution and jointly estimates uncertainties in both spike timing and model parameters from fast calcium ...