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amsi-winter-school

Materials for the AMSI Winter School

bayesian-synthetic-likelihood

Code for the Bayesian Synthetic Likelihood paper by Price et al 2016 to appear in JCGS

bsl

Bayesian synthetic likelihood (BSL, Price et al (2018)) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular.

cvbookchapter

Companion code for the Control Variates for MCMC book chapter

debiasedhmc

Unbiased HMC estimators with couplings

iconsforsupporttable

independent-smc

Some example code for the paper "Sequential Monte Carlo Samplers with Independent Markov Chain Monte Carlo Proposals"

posterior

The posterior R package

rcppsmc

Rcpp bindings for Sequential Monte Carlo

rcpptests

Test Kalman Filter Implementations

sgmtlinear

Sparse multi-task linear regression

smc-ns

Unbiased and Consistent Nested Sampling via Sequential Monte Carlo

zvcv

R package for derivative-based variance reduction