# monte_carlo

Neural denoising diffusion models
Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models…
2021-11-11
– 2023-12-06Random number generation
2015-05-14
– 2023-09-08Annealing in inference
Tempering, cooling, Platt scaling…
2020-09-30
– 2023-09-04Monte Carlo methods
2014-11-16
– 2023-08-26Saying “Bayes” is not enough
The other secret steps to doing Bayesian statistics
2016-05-30
– 2023-05-19Reparameterization methods for MC gradient estimation
Pathwise gradient estimation,
2018-04-04
– 2023-05-02Normalizing flows
Invertible density models, sounding clever by using the word diffeomorphism like a real mathematician
2018-04-04
– 2023-05-02Monte Carlo gradient estimation
2020-09-30
– 2023-05-02Bayesian posterior inference via optimisation
Conditioning by gradient
2020-08-17
– 2023-04-28Particle filters
incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names
2014-07-25
– 2023-03-24Ensemble Kalman methods
Data Assimilation; Data fusion; Sloppy filters for over-ambitious models
2015-06-22
– 2023-03-18Generative flow nets
Gflownets
2021-11-11
– 2023-02-13(Kernelized) Stein variational gradient descent
KSVD, SVGD
2022-11-02
– 2023-01-09Hamiltonian and Langevin Monte Carlo
Physics might be on to something
2018-07-31
– 2022-11-14Langevin dynamcs MCMC
2020-08-17
– 2022-10-01Score matching
2021-11-11
– 2022-09-23Ensemble Kalman methods for training neural networks
Data assimilation for network weights
2022-09-20Laplace approximations in inference
Lightweight uncertainties, especially for heavy neural nets
2021-07-28
– 2022-09-06Generic variance reduction in Monte Carlo samplers
2022-08-25Bayes linear methods
some kind of approximate Bayes thing
2022-08-17
– 2022-08-18Integrated Nested Laplace Approximation
2021-07-28
– 2022-07-26Bayes for beginners
2016-05-30
– 2022-07-23Markov Chain Monte Carlo methods
2017-08-28
– 2022-06-08Nested sampling
2022-05-16System identification using particle filters
A.k.a. parameter estimation in data assimilation
2014-07-25
– 2022-05-04Generalized Bayesian Computation
2019-10-03
– 2022-04-28Inference without KL divergence
2019-10-03
– 2022-04-28Particle belief propagation
Graphical inference using empirical distribution estimates
2014-07-25
– 2022-04-08Particle Markov Chain Monte Carlo
Particle systems as MCMC proposals
2014-07-25
– 2022-04-08Bayesian nonparametric statistics
Updating more dimensions than datapoints
2016-05-30
– 2022-04-07Change points
Looking for regime changes in stochastic processes. a.k.a. Switching state space models
2021-11-29
– 2022-04-01Probabilistic programming
Doing statistics using the tools of computer science
2019-10-02
– 2022-02-11Pyro
Approximate maximum in the density of probabilistic programming effort
2019-10-02
– 2021-11-25Deep generative models
2020-12-10
– 2021-11-11Approximate Bayesian Computation
Posterior updates without likelihood
2020-08-25
– 2021-09-20Energy based models
Inference with kinda-tractable un-normalized densities
2021-06-07Determinantal point processes
2018-07-31
– 2021-03-16Log concave distributions
associated tools
2017-08-28
– 2021-03-11Feynman-Kac formulae
2021-01-27Generative adversarial networks
2016-10-07
– 2020-12-14Markov Chain Monte Carlo methods
2020-06-28
– 2020-10-28Stan
The flagship Bayesian workhorse
2020-10-19Monte Carlo optimisation
2020-09-30Splitting simulation
2017-05-29
– 2020-09-28Combinatorics of note
2020-07-18Adaptive Markov Chain Monte Carlo samplers
2020-04-28
– 2020-04-30Tuning an MCMC sampler
2020-04-30
– 2020-04-30The cross entropy method
2020-04-24Effective sample size
2016-11-21
– 2020-03-03Permanental point processes
2019-12-04Rare-event-conditional estimation
2017-05-29
– 2017-11-10Quasi Monte Carlo
2015-09-01
– 2017-02-14