4 papers
A Decision-Theoretic Framework for Comparing Likelihood Ratio Methods for the Rare Type Match Problem
Giulia Cereda, Fabio Corradi, Cecilia Viscardi
The rare type match problem is a challenging situation faced by a forensic statistician who aims at providing the value of a match between some characteristic of a crime stain and…
Full Bayesian Reinforcement Learning via LF-IBIS
Stefano Masini, Cecilia Viscardi, Michela Baccini
Reinforcement Learning (RL) is a sequential decision-making framework in which an agent learns optimal policies through interaction with an environment by maximizing cumulative rew…
A Comparison of Kernels for ABC-SMC
Dennis Prangle, Cecilia Viscardi, Sammy Ragy
A popular method for likelihood-free inference is approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithms. These approximate the posterior using a population o…
Distilling Importance Sampling for Likelihood Free Inference
Dennis Prangle, Cecilia Viscardi
Likelihood-free inference involves inferring parameter values given observed data and a simulator model. The simulator is computer code which takes parameters, performs stochastic…