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ABC Reinforcement Learning
ABC Reinforcement Learning
2013/4/28
This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is that we only require a prio...
abc: an R package for Approximate Bayesian Computation (ABC)
abc package Approximate Bayesian Computation
2011/7/7
Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data.
Considerate Approaches to Achieving Sufficiency for ABC model selection
Considerate Approaches Achieving Sufficiency ABC model selection
2011/7/6
For nearly any challenging scientific problem evaluation of the likelihood is problematic if not impossible. Approximate Bayesian computation (ABC) allows us to employ the whole Bayesian formalism to ...
Sequential Monte Carlo (SMC) approaches have become work horses in approximate Bayesian computation (ABC). Here we discuss how to construct the perturbation kernels that are required in ABC-SMC approa...
Lack of confidence in ABC model choice
Lack of confidence ABC model choice Approximate Bayesian computation
2011/3/24
Approximate Bayesian computation (ABC) have become a essential tool for the analysis of complex stochastic models. Earlier, Grelaud et al. (2009) advocated the use of ABC for Bayesian model choice in ...
ABC likelihood-free methods for model choice in Gibbs random fields
Approximate Bayesian Computation model choice Gibbs Random Fields Bayes factor protein folding
2009/9/24
Gibbs random fields (GRF) are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when tho...