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

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

Reparameterization tricks in inference Pathwise gradient estimation, nNormalizing flows, invertible density models, inference by measure transport, low-dimensional coupling… 2018-04-04 – 2023-01-19

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

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

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

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

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

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

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

Probabilistic neural nets Bayesian and other probabilistic inference in overparameterized ML 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

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

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

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

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