probabilistic_algorithms

Feynman-Kac formulae 2021-01-27

Infinite width limits of neural networks 2020-12-09 – 2021-01-05

Reparameterization tricks in inference Normalizing flows, invertible density models, inference by measure transport, low-dimensional coupling… 2018-04-04 – 2020-12-18

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

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

Recommender systems 2020-11-30

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

Bootstrap Shuffling reality to produce your data 2014-11-26 – 2020-10-16

Differentiating through the Gamma 2020-06-12 – 2020-10-15

Monte Carlo gradient estimation 2020-09-30

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

Markov Chain Monte Carlo methods 2017-08-28 – 2020-04-28

The cross entropy method 2020-04-24

Particle filters incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names 2014-07-25 – 2020-04-08

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

Random 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

Random neural networks 2017-02-17 – 2017-02-19

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

Randomised linear algebra 2016-08-16

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

Monte Carlo methods 2014-11-16 – 2015-12-30

Biomimetic algorithms 2015-12-22

Expectation propagation 2015-10-26

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