monte_carlo

Approximate Bayesian Computation Posterior updates without likelihood 2020-08-25 – 2021-09-03

Monte Carlo methods 2014-11-16 – 2021-09-02

Laplace approximations in inference Posterior updates without likelihood 2021-07-28

Deep generative models 2020-12-10 – 2021-06-08

Energy based models Inference with kinda-tractable un-normalized densities 2021-06-07

Inference without KL divergence 2019-10-03 – 2021-04-23

Log concave distributions associated tools 2017-08-28 – 2021-03-11

Markov Chain Monte Carlo methods 2017-08-28 – 2021-03-11

Probabilistic programming Programming with probability distributions for, e.g. Bayesian inference 2019-10-02 – 2021-03-10

Feynman-Kac formulae 2021-01-27

Generative adversarial networks 2016-10-07 – 2020-12-14

Markov Chain Monte Carlo methods 2020-06-28 – 2020-10-28

Stan The flagship Bayesian workhorse 2020-10-19

Monte Carlo gradient estimation 2020-09-30

Monte Carlo optimisation 2020-09-30

Splitting simulation 2017-05-29 – 2020-09-28

Combinatorics of note 2020-07-18

Adaptive Markov Chain Monte Carlo samplers 2020-04-28 – 2020-04-30

Tuning an MCMC sampler 2020-04-30 – 2020-04-30

The cross entropy method 2020-04-24

Particle filters incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names 2014-07-25 – 2020-04-08

Effective sample size 2016-11-21 – 2020-03-03

Bayes for beginners 2016-05-30 – 2020-01-30

Markov Chains 2019-12-23 – 2019-10-18

Hamiltonian and Langevin Monte Carlo Physics might be on to something 2018-07-31 – 2018-07-12

Rare-event-conditional estimation 2017-05-29 – 2017-11-10

Gaussian Process simulation and circulant embeddings I might shoehorn Whittle likelihoods in here too 2017-11-10 – 2017-11-10

Quasi Monte Carlo 2015-09-01 – 2017-02-14

Random number generation 2015-05-14 – 2015-10-13