monte_carlo

Ensemble Kalman methods Data Assimilation; Data fusion; Sloppy filters for over-ambitious models 2015-06-22 – 2023-01-24

(Kernelized) Stein variational gradient descent KSVD, SVGD 2022-11-02 – 2023-01-09

Hamiltonian and Langevin Monte Carlo Physics might be on to something 2018-07-31 – 2022-11-14

Langevin dynamcs MCMC 2020-08-17 – 2022-10-01

Neural denoising diffusion models Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models… 2021-11-11 – 2022-09-24

Score matching 2021-11-11 – 2022-09-23

Bayesian posterior sampling via SGD One of those times when the easy thing can also be the smart thing 2020-08-17 – 2022-09-21

Ensemble Kalman methods for training neural networks Data assimilation for network weights 2022-09-20

Laplace approximations in inference Lightweight uncertainties, especially for heavy neural nets 2021-07-28 – 2022-09-06

Generic variance reduction in Monte Carlo samplers 2022-08-25

Bayes linear methods some kind of approximate Bayes thing 2022-08-17 – 2022-08-18

Monte Carlo gradient estimation Especially stochastic automatic differentiation 2020-09-30 – 2022-08-18

Integrated Nested Laplace Approximation 2021-07-28 – 2022-07-26

Bayes for beginners 2016-05-30 – 2022-07-23

Markov Chain Monte Carlo methods 2017-08-28 – 2022-06-08

Nested sampling 2022-05-16

System identification using particle filters A.k.a. parameter estimation in data assimilation 2014-07-25 – 2022-05-04

Generalized Bayesian Computation 2019-10-03 – 2022-04-28

Inference without KL divergence 2019-10-03 – 2022-04-28

Saying “Bayes” is not enough Bayesians are usually not actually doing Bayesian reasoning well and even if we were, it would be insufficient to do science, or life 2016-05-30 – 2022-04-15

Particle filters incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names 2014-07-25 – 2022-04-10

Particle belief propagation Graphical inference using empirical distribution estimates 2014-07-25 – 2022-04-08

Particle Markov Chain Monte Carlo Particle systems as MCMC proposals 2014-07-25 – 2022-04-08

Bayesian nonparametric statistics Updating more dimensions than datapoints 2016-05-30 – 2022-04-07

Change points Looking for regime changes in stochastic processes. a.k.a. Switching state space models 2021-11-29 – 2022-04-01

Generative flow 2022-03-07

Probabilistic programming Doing statistics using the tools of computer science 2019-10-02 – 2022-02-11

Pyro Approximate maximum in the density of probabilistic programming effort 2019-10-02 – 2021-11-25

Deep generative models 2020-12-10 – 2021-11-11

Approximate Bayesian Computation Posterior updates without likelihood 2020-08-25 – 2021-09-20

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

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

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

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 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

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

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

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

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