A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses

08/07/2020

∙

This article presents a Bayesian inferential method where the likelihood for a model is unknown but where data can easily be simulated from the model. We discretize simulated (continuous) data to estimate the implicit likelihood in a Bayesian analysis employing a Markov chain Monte Carlo algorithm. Three examples are presented as well as a small study on some of the method's properties.

READ FULL TEXT

A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses

Bayesian Restoration of Digital Images Employing Markov Chain Monte Carlo a Review

A Data-driven Diagnostic Framework for Small Watersheds based on Markov Chain-Monte Carlo

ABC Learning of Hawkes Processes with Missing or Noisy Event Times

A stochastic model of human visual attention with a dynamic Bayesian network

Modelling intransitivity in pairwise comparisons with application to baseball data

Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection

The polar-generalized normal distribution

A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses

Related Research

Bayesian Restoration of Digital Images Employing Markov Chain Monte Carlo a Review

A Data-driven Diagnostic Framework for Small Watersheds based on Markov Chain-Monte Carlo

ABC Learning of Hawkes Processes with Missing or Noisy Event Times

A stochastic model of human visual attention with a dynamic Bayesian network

Modelling intransitivity in pairwise comparisons with application to baseball data

Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection

The polar-generalized normal distribution