graphical_models

Adverse advice selection 2022-01-25 – 2022-11-15

Interaction effects and subgroups are probably what we want to estimate 2022-01-25 – 2022-11-06

Continuous and equilibrium probabilistic graphical models 2014-08-05 – 2022-10-10

Learning graphical models from data Also, causal discovery, structure discovery 2017-09-20 – 2022-10-01

Factor graphs 2019-12-16 – 2022-09-06

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

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

Ablation studies, lesion studies, and complex systems 2016-10-26 – 2022-08-06

Causal inference on DAGs Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-08-06

Instumental variables and two stage regression Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-08-06

Bayes linear regression and basis-functions in Gaussian process regression a.k.a Fixed Rank Kriging, weight space GPs 2022-02-22 – 2022-07-27

Expectation propagation Generalized moment matching 2015-10-26 – 2022-05-04

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

Vecchia factoring of GP likelihoods Ignore some conditioning in the dependencies and attain a sparse cholesky factor for the precision matrix 2022-04-27

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

Partition-valued random variates 2022-04-01

Belief propagation 2014-11-25 – 2022-03-31

Visualising probabilistic graphical models Also related models, such as Neural nets 2018-03-29 – 2022-02-18

Message-passing algorithms in graphical models Cleaving reality at the joint, then summing it at the marginal 2014-11-25 – 2022-02-17

Probabilistic graphical models 2014-08-05 – 2022-02-08

Random graphical models Causality in amongst confusion 2021-10-26 – 2022-02-07

Statistics and machine learning 2011-04-15 – 2022-01-27

Causality via potential outcomes Neyman-Rubin, counterfactuals, conditional treatment effects, and related tricks 2016-10-26 – 2021-12-10

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

External validity and dataset shift Also transfer learning, learning under covariate shift, transferable learning, domain adaptation etc 2020-10-17 – 2021-11-03

Efficient factoring of GP likelihoods 2020-10-16 – 2020-10-26

Independence, conditional, statistical 2016-04-21 – 2020-09-13

Causal Bayesian networks Staged tree models, probability trees …Causalan Bayesian networks 2020-11-01 – 2020-09-01

Directed graphical models 2017-09-20 – 2020-05-13

Undirected graphical models 2017-09-20 – 2019-10-28

Quantum-probabilistic graphical models 2017-08-07

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