Bayesian methods in statistics

In which beliefs are probabilities too

Neural denoising diffusion models Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models… 2021-11-11 – 2023-12-06

Simulation-based inference 2014-12-23 – 2023-11-23

Multi fidelity models Data-driven multi-scale sampling, multi-resolution, super-resolution 2020-08-24 – 2023-11-20

Quantization 2016-03-29 – 2023-11-02

Bayesian inference for misspecified models 2016-05-30 – 2023-09-20

Annealing in inference Tempering, cooling, Platt scaling… 2020-09-30 – 2023-09-04

Monte Carlo methods 2014-11-16 – 2023-08-26

Learnable coarse-graining Approximate meso-scale physics 2020-08-24 – 2023-08-01

M-open, M-complete, M-closed 2016-05-30 – 2023-07-23

Hypothesis tests, statistical 2014-08-23 – 2023-07-18

Bayes neural nets via subsetting weights 2017-01-11 – 2023-07-03

Noise contrastive estimation Also “negative sampling”. 2020-04-22 – 2023-06-06

Tabular data processing in python CSV to Data lake 2020-11-30 – 2023-05-29

Recurrent / convolutional / state-space Translating between means of approximating time series dynamics 2016-04-05 – 2023-05-24

State filtering for hidden Markov models Kalman and friends 2015-06-22 – 2023-05-24

Saying “Bayes” is not enough The other secret steps to doing Bayesian statistics 2016-05-30 – 2023-05-19

Belief propagation 2014-11-25 – 2023-05-17

Canonical correlation 2014-08-23 – 2023-05-17

Method of Adjoints for differentiating through ODEs 2017-09-15 – 2023-05-15

Conformal prediction 2016-12-26 – 2023-05-05

Reparameterization methods for MC gradient estimation Pathwise gradient estimation, 2018-04-04 – 2023-05-02

Normalizing flows Invertible density models, sounding clever by using the word diffeomorphism like a real mathematician 2018-04-04 – 2023-05-02

Monte Carlo gradient estimation 2020-09-30 – 2023-05-02

Bayesian posterior inference via optimisation Conditioning by gradient 2020-08-17 – 2023-04-28

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2023-04-27

Recursive identification Learning forward dynamics by looking at time series. 2017-09-15 – 2023-04-16

Expectation propagation Generalized moment matching 2015-10-26 – 2023-04-03

Particle filters incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names 2014-07-25 – 2023-03-24

Ensemble Kalman methods Data Assimilation; Data fusion; Sloppy filters for over-ambitious models 2015-06-22 – 2023-03-18

Transport maps Inference by measure transport, low-dimensional coupling… 2018-04-04 – 2023-02-21

Generative flow nets Gflownets 2021-11-11 – 2023-02-13

Last-layer Bayes neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2023-02-09

Variational message-passing algorithms in graphical models Cleaving reality at the joint, then summing it at the marginal 2014-11-25 – 2023-01-12

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

Transforms of Gaussian noise Delta method, error propagation, unscented transform, Taylor expansion… 2014-11-25 – 2022-12-23

Bayesian model selection by model evidence maximisation Type II maximum likelihood, marginal maximum likelihood, Bayes Occam’s razor 2017-08-20 – 2022-12-22

Density ratio tricks 2022-12-06

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

Bayesian sparsity 2019-01-08 – 2022-10-25

Online learning 2018-09-30 – 2022-10-20

Neural tangent kernel 2020-12-09 – 2022-10-14

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

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

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

Penalised/regularised regression 2016-06-23 – 2022-09-19

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

Gaussian belief propagation Least squares at maximal elaboration 2014-11-25 – 2022-09-01

Factorial hidden Markov models 2022-08-29

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

Elliptical belief propagation Generalized least generalized squares 2022-08-22 – 2022-08-23

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

Recommender systems 2020-11-30 – 2022-08-15

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

Applied psephology 2016-07-12 – 2022-05-25

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

SLAM Simultaneous Location and Mapping 2014-11-25 – 2022-04-28

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

Probabilistic neural nets Inferring distributions in neural nets 2017-01-11 – 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

Gaussian processes on lattices 2019-10-30 – 2022-02-28

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

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 Processes as stochastic differential equations Imposing time on things 2019-09-18 – 2021-11-25

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

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

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

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

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

Random fields as stochastic differential equations Precision vs covariance, fight! 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

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

Matching and weighting Making the optimal beverage from the fruit life gave you 2020-04-22

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

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

Defining dynamics via Gaussian processes 2019-09-18

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

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

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

Clustering 2015-05-22 – 2016-06-07

Coarse graining 2014-11-11 – 2015-12-02