probabilistic_algorithms

Nested sampling 2022-05-16

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

Expectation propagation Generalized moment matching 2015-10-26 – 2022-05-04

Laplace approximations in inference Lightweight uncertainties, especially for heavy neural nets 2021-07-28 – 2022-04-10

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

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

Pólya-Gamma augmentation trick 2017-02-20 – 2022-04-01

Reservoir Computing 2022-03-28

Genetic programming 2015-12-22 – 2022-03-09

Generative flow 2022-03-07

Variational inference On fitting something not too far from a pretty good model that is not too hard 2016-03-22 – 2022-02-10

Recommender systems 2020-11-30 – 2022-02-04

Bootstrap Shuffling reality to produce your data 2014-11-26 – 2022-01-27

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

Overparameterization a.k.a. improper learning 2018-04-04 – 2021-12-08

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

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

Random neural networks 2017-02-17 – 2021-10-12

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

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

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

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

Neural net kernels 2019-09-16 – 2021-05-24

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

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

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

Feynman-Kac formulae 2021-01-27

Random embeddings and hashing 2016-12-05 – 2020-12-01

Randomised regression 2017-01-13 – 2020-12-01

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

Monte Carlo optimisation 2020-09-30

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

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14 – 2020-09-18

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

Combinatorics of note 2020-07-18

Variational inference On fitting the best model one can be bothered to 2016-03-22 – 2020-05-24

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

Bias reduction Estimating the bias of an estimator so as to subtract it off again 2020-02-26

Random (element) matrix theory 2014-11-09 – 2019-10-10

Nearly sufficient statistics How about “Sufficient sufficiency”? — is that taken? 2018-03-13 – 2019-01-14

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

Compressed sensing and sampling A fancy ways of counting zero 2014-08-18 – 2017-06-14

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

Randomised linear algebra 2016-08-16

Kernel approximation 2016-07-27

Expectation maximisation 2014-08-17 – 2016-04-17

Biomimetic algorithms 2015-12-22

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