Ensemble Kalman methods Data Assimilation; Data fusion; Sloppy filters for over-ambitious models 2015-06-22 – 2022-09-26
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
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