Bayesian methods in statistics

Bayesian nonparametric statistics Updating more dimensions than datapoints 2016-05-30 – 2022-01-14

Laplace approximations in inference Lightweight uncertainties, especially for heavy neural nets 2021-07-28 – 2022-01-13

Recommender systems 2020-11-30 – 2022-01-12

Reparameterization tricks in inference Pathwise gradient estimation, nNormalizing flows, invertible density models, inference by measure transport, low-dimensional coupling… 2018-04-04 – 2021-12-21

Monte Carlo gradient estimation Especially stochastic automatic differentiation 2020-09-30 – 2021-12-20

Variational state filtering 2018-03-19 – 2021-12-08

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

Gaussian Process regression via state filtering Imposing time on things 2019-09-18 – 2021-11-25

Ensembling neural nets Monte Carlo 2020-12-14 – 2021-11-25

Gaussian processes on lattices 2019-10-30 – 2021-11-25

Probabilistic programming Doing statistics using the tools of computer science 2019-10-02 – 2021-11-17

Neural diffusion models 2021-11-11 – 2021-11-11

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

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2021-11-03

Survey modelling Adjusting for the Lizardman constant 2019-08-29 – 2021-10-24

Planning under uncertainty Transcending planning-though-hope-and-haruspicy 2020-04-23 – 2021-10-10

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

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

Uncertainty quantification 2016-12-26 – 2021-07-06

Learning on tabular data 2020-11-30 – 2021-06-21

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

Infinite width limits of neural networks 2020-12-09 – 2021-05-11

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

Random fields as stochastic differential equations 2020-10-12 – 2021-03-01

Chaos expansions Polynomial chaos, generalized polynomial chaos, arbitrary chaos etc 2020-05-21 – 2021-02-15

Feynman-Kac formulae 2021-01-27

Probabilistic spectral analysis 2019-11-13 – 2020-11-25

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

Variational autoencoders 2019-11-04 – 2020-09-10

Online learning 2018-09-30 – 2020-08-26

Minimum description length 2020-08-06

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

Mixture models for density estimation 2016-03-29 – 2020-04-24

Likelihood free inference 2020-04-22

Post stratification Making the optimal beverage from the fruit life gave you 2020-04-22

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

Bayes for beginners 2016-05-30 – 2020-01-31

Applied psephology 2016-07-12 – 2020-01-20

Optimal control 2015-06-22 – 2019-11-01

Frequentist consistency of Bayesian methods TFW two flawed methods for understanding the world agree with at least each other 2016-04-12 – 2019-10-19

M-open, M-complete, M-closed 2016-05-30 – 2019-10-18

State filtering parameters Tracking things that don’t move 2017-09-15 – 2019-10-01

Defining dynamics via Gaussian processes 2019-09-18

Bayesian model selection 2017-08-20 – 2019-07-22

Bayesian sparsity 2019-01-08

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

State filtering for hidden Markov models Kalman and friends 2015-06-22 – 2017-07-06

Marketing psychology 2017-04-27 – 2017-05-29

Expectation propagation 2015-10-26