statistics

GUIs for numerical array data 2015-03-04 – 2022-05-19

Database and data file GUIs 2015-03-04 – 2022-05-19

Pytorch #torched 2018-05-04 – 2022-05-18

Learning graphical models from data Also, causal discovery, structure discovery 2017-09-20 – 2022-05-18

Nested sampling 2022-05-16

Approximate matrix factorisation Sometimes even exact 2014-08-05 – 2022-05-14

Exponential families 2016-04-19 – 2022-05-13

Smooth transforms of Gaussian noise Delta method, error propagation, unscented transform, Taylor expansion, in finite and inifinite dimensional spaces 2014-11-25 – 2022-05-12

Forecasting Vegan haruspicy 2015-06-16 – 2022-05-11

Ensemble Kalman methods Sloppy filters for over-ambitious models 2015-06-22 – 2022-05-10

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

(Discrete-measure)-valued stochastic processes 2019-10-10 – 2022-05-04

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

Forecasting with model averaging Mixture of experts, ensembles and time series 2022-05-04

Anthropic principles Convenience-sampling lived human experience 2020-02-15 – 2022-05-01

Farming and husbandry of black swans and dragon kings Heavy tailed and Knightian uncertainties for fun and profit 2020-09-22 – 2022-04-30

Causal graphical model reading group 2022 Causal inference 2022-04-01 – 2022-04-29

Generalized Bayesian Computation 2019-10-03 – 2022-04-28

Inference without KL divergence 2019-10-03 – 2022-04-28

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

Statistics and ML in python 2015-04-27 – 2022-04-22

Basis-functions in Gaussian process regression a.k.a Fixed Rank Kriging, basis function regression, weight space, spatial random effects 2022-02-22 – 2022-04-21

Anomaly detection I don’t define what is abnormal, but I know it when I see it 2015-10-06 – 2022-04-21

Hierarchical models DAGs, multilevel models, random coefficient models, mixed effect models, structural equation models… 2015-06-07 – 2022-04-21

Generalised Ornstein-Uhlenbeck processes AR(1)-like processes 2022-01-10 – 2022-04-17

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

Laplace approximations in inference Lightweight uncertainties, especially for heavy neural nets 2021-07-28 – 2022-04-10

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

Particle Markov Chain Monte Carlo Particle systems as MCMC proposals 2014-07-25 – 2022-04-08

Bayesian nonparametric statistics Updating more dimensions than datapoints 2016-05-30 – 2022-04-07

Inverse problems in function space a.k.a. Bayesian calibration, model uncertainty for PDEs and other wibbly surfaces 2020-10-13 – 2022-04-07

Science communication On making people feel they are smart enough to teach themselves, or failing that, that they are smart enough to fund YOU to teach yourself 2021-06-02 – 2022-04-05

Predictive coding Does the model that our brains do bayesian variational prediction make any actual predictions about our brains? 2011-11-27 – 2022-04-04

Gumbel (soft) max tricks Concrete distribution, relaxed categorical etc 2017-02-20 – 2022-04-01

Change points Looking for regime changes in stochastic processes. a.k.a. Switching state space models 2021-11-29 – 2022-04-01

Pólya-Gamma augmentation trick 2017-02-20 – 2022-04-01

Detecting stationarity in stochastic processes Change-points, trends and transients 2021-11-29 – 2022-04-01

Partition-valued random variates 2022-04-01

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

Random binary vectors The class of distributions that cause you to reinvent Shannon information if you stare at them long enough 2017-02-20 – 2022-03-30

Measure-valued random variates Including completely random measures and many generalizations 2020-10-16 – 2022-03-30

Gaussian belief propagation Least squares at maximal elaboration 2014-11-25 – 2022-03-28

Classification Computer says no 2017-02-20 – 2022-03-28

Causal inference on DAGs Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-03-08

Generative flow 2022-03-07

Data dashboards and ML demos On assuring the client that you are doing something data-sciency because it looks like in the movies 2020-03-12 – 2022-03-07

Survival analysis and reliability Hazard rates, proportional hazard regression, life testing, mean time to failure 2019-03-12 – 2022-03-07

Plotting in R 2019-10-13 – 2022-03-05

Self-supervised learning I just wanna be meeeeee / with high probabilityyy ♬♪ 2022-03-04

Interaction effects are probably what we want to estimate 2022-01-25 – 2022-03-04

Gaussian processes on lattices 2019-10-30 – 2022-02-28

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2022-02-25

Stability in dynamical systems Lyapunov exponents and ilk 2019-05-21 – 2022-02-22

Optimal transport inference I feel the earth mover under my feet, I feel the ψ tumbling down, I feel my heart start to trembling, Whenever you’re around my empirical density in minimal transport cost 2021-03-16 – 2022-02-22

Bayes for beginners 2016-05-30 – 2022-02-19

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

M-estimation 2016-07-11 – 2022-02-17

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

Politics as statistical learner 2022-01-31 – 2022-02-17

System identification in continuous time Learning in continuous ODEs, SDEs and CDEs 2016-08-01 – 2022-02-15

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

Risk perception and communication 2011-04-14 – 2022-02-11

Variational inference On fitting something not too far from a pretty good model that is not too hard 2016-03-22 – 2022-02-10

Factor graphs 2019-12-16 – 2022-02-09

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

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

Recommender systems 2020-11-30 – 2022-02-04

Karhunen-Loève expansions 2019-09-16 – 2022-02-01

Applied psephology 2016-07-12 – 2022-01-30

Spatial processes and statistics thereof 2011-07-29 – 2022-01-28

Spatial data in R 2021-03-21 – 2022-01-28

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

Feedback system identification, not necessarily linear Learning dynamics from data 2016-08-01 – 2022-01-27

Hypothesis tests, statistical 2014-08-23 – 2022-01-27

Bootstrap Shuffling reality to produce your data 2014-11-26 – 2022-01-27

Teaching mathematics and statistics 2020-02-12 – 2022-01-27

Machine learning and statistics in Julia 2019-11-27 – 2022-01-27

Learning on manifolds Finding the lowest bit of a krazy straw, from the inside 2011-10-21 – 2022-01-26

Psychometrics Dimensionality reduction for souls 2017-10-31 – 2022-01-26

Feedback system identification, linear 2016-07-27 – 2022-01-21

(Outlier) robust statistics 2014-11-25 – 2022-01-21

Risk perception and communication 2011-04-14 – 2022-01-17

Bayesian inverse problems 2016-03-30 – 2022-01-13

Categorical random variates 2017-02-20 – 2022-01-12

Statistical projectivity 2020-04-26 – 2022-01-11

Generalised autoregressive processes 2022-01-10

Mind as statistical learner 2022-01-09

R packaging, installation etc 2020-11-30 – 2022-01-07

Reparameterization tricks in inference Pathwise gradient estimation, nNormalizing flows, invertible density models, inference by measure transport, low-dimensional coupling… 2018-04-04 – 2021-12-21

Garbled highlights from NeurIPS 2021 2021-11-05 – 2021-12-15

R The statistical programming language, not the letter 2011-08-07 – 2021-12-14

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

Causal inference in highly parameterized ML 2020-09-18 – 2021-12-10

Variational state filtering 2018-03-19 – 2021-12-08

Convolutional subordinator processes 2021-03-08 – 2021-12-01

Pyro Approximate maximum in the density of probabilistic programming effort 2019-10-02 – 2021-11-25

Combining kernels 2019-09-16 – 2021-11-25

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

Neural diffusion models 2021-11-11 – 2021-11-11

Deep generative models 2020-12-10 – 2021-11-11

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

Data cleaning 90% of statistics 2020-01-22 – 2021-10-28

Probably Approximately Correct 2014-11-24 – 2021-10-27

Survey modelling Adjusting for the Lizardman constant 2019-08-29 – 2021-10-24

Neural music synthesis 2016-01-15 – 2021-10-14

(Geo)spatial data sets In which I complain about paying a nominal fee for giant rocket robots that scan the earth from space 2021-03-02 – 2021-10-13

Missing data Imputation, estimation despite etc 2021-10-07

Mind as statistical learner 2020-06-23 – 2021-10-05

The Gaussian distribution The probability distribution that you give up and use in the end 2016-06-27 – 2021-10-03

Algorithmic statistics Probably also algorithmic information theory 2014-07-25 – 2021-09-26

Science for policy Using evidence and reason to govern ourselves 2011-08-07 – 2021-09-22

Fractals and self-similarity 2011-11-13 – 2021-09-22

Approximate Bayesian Computation Posterior updates without likelihood 2020-08-25 – 2021-09-20

Heavy tails Weird things about rare massive events 2020-01-13 – 2021-09-18

Fractional differential equations 2016-03-22 – 2021-09-13

Inverse problems 2016-03-30 – 2021-08-23

VS Code as R IDE 2021-10-06 – 2021-08-22

Convolutional stochastic processes Moving averages of noise 2021-03-01 – 2021-08-16

Rough path theory Also, signatures. 2021-04-02 – 2021-08-05

Generalised linear models 2016-03-24 – 2021-08-05

Models for count data 2015-05-14 – 2021-08-03

Publication bias Replication crises, P-values, bitching about journals, other debilities of contemporary science at large 2016-08-30 – 2021-07-22

IDEs for R Friendly UIs for the almost-friendly statistical programming language 2011-08-07 – 2021-07-18

Contagion processes and their statistics 2016-08-30 – 2021-07-15

Media virality Strategic modelling for content creators 2016-08-30 – 2021-07-15

Learning summary statistics 2020-04-22 – 2021-07-15

Tensorflow The framework to use for deep learning if you groupthink like Google 2016-07-11 – 2021-07-07

Graph sampling Estimating functionals of graphs 2020-02-15 – 2021-07-06

Uncertainty quantification 2016-12-26 – 2021-07-06

Semi/weakly-supervised learning On extracting nutrition from bullshit 2017-07-24 – 2021-07-05

Extreme value theory On the decay of awfulness with oftenness 2020-01-13 – 2021-06-30

Learning on tabular data 2020-11-30 – 2021-06-21

Optimal transport metrics Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances 2019-05-30 – 2021-06-08

Energy based models Inference with kinda-tractable un-normalized densities 2021-06-07

Stein’s method 2021-03-12 – 2021-06-01

Neural net kernels 2019-09-16 – 2021-05-24

Cross validation 2016-09-05 – 2021-05-13

Probability divergences Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses 2014-11-25 – 2021-05-12

Spectral factorization 2021-05-05 – 2021-05-07

Wiener-Hopf method Righteous hack for certain integral equations 2021-05-05

Prediction processes Some kind of weird time series formalism 2021-04-09

Dynamical systems via Koopman operators Composition operators, Dynamic Extended Mode decompositions… 2020-10-13 – 2021-04-09

Kernel zoo 2019-09-16 – 2021-03-30

Generically approximating probability distributions 2021-03-12 – 2021-03-22

Statistics, computational complexity thereof 2020-11-03 – 2021-03-10

Mind reading by computer The ultimate inverse problem 2017-07-01 – 2021-03-03

Convolutional Gaussian processes 2021-03-01

Random fields as stochastic differential equations Precision vs covariance, fight! 2020-10-12 – 2021-03-01

Stochastic processes on manifolds 2021-03-01

Learning covariance functions Learning a family of covariances at once 2019-09-16 – 2021-03-01

Causal inference in the continuous limit 2021-02-17

Stability in linear dynamical systems This Bodes well 2019-07-19 – 2021-02-16

Feynman-Kac formulae 2021-01-27

Miscellaneous nonstationary kernels 2019-09-16 – 2021-01-21

Warping of stationary stochastic processes 2019-09-16 – 2021-01-21

Bayesians vs frequentists Just because we both get the same answer doesn’t mean neither of us is wrong 2014-11-25 – 2021-01-14

Statistical mechanics of statistics 2016-12-01 – 2021-01-06

Covariance functions Variograms, Mercer kernels, positive definite operators, spare reproducing kernels for that Hilbert space I bought on eBay real cheap 2019-09-16 – 2021-01-05

Garbled highlights from NeurIPS 2020 2020-09-17 – 2020-12-11

Data sets Questions for answers looking for questions 2015-06-26 – 2020-12-02

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

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

Distribution regression 2020-12-01

Probabilistic spectral analysis 2019-11-13 – 2020-11-25

Observability and sensitivity in learning dynamical systems Parameter identifiability in dynamical models 2020-11-09

Weighted data in statistics 2020-11-04 – 2020-11-06

ELBO Evidence lower bound, variational free energy etc 2020-10-02 – 2020-10-28

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

Stan The flagship Bayesian workhorse 2020-10-19

Sparse model selection 2016-09-05 – 2020-10-02

Quantitative risk measurement Mathematics of actuarial and financial disaster 2015-04-30 – 2020-09-22

Filter design, linear Especially digital 2017-07-24 – 2020-09-18

Data summarization On maps drawn at smaller than 1:1 scale 2019-01-14 – 2020-09-18

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

Statistics of spatio-temporal processes 2020-09-11 – 2020-09-11

Dimensionality reduction Wherein I teach myself, amongst other things, feature selection, how a sparse PCA works, and decide where to file multidimensional scaling 2015-03-22 – 2020-09-11

Variational autoencoders 2019-11-04 – 2020-09-10

Causal graphical model reading group 2020 An introduction to conditional independence DAGs and their use for causal inference. 2020-08-30 – 2020-09-03

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

Online learning 2018-09-30 – 2020-08-26

Minimum description length 2020-08-06

Plotting stuff in julia 2019-05-31 – 2020-07-25

Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23

Model complexity penalties Information criteria, degrees of freedom etc 2015-04-22 – 2020-06-22

Stochastic signal sampling Discrete sample representation of continuous stochastic processes 2017-05-30 – 2020-06-11

Long memory time series 2011-11-13 – 2020-05-28

Variational inference On fitting the best model one can be bothered to 2016-03-22 – 2020-05-24

Lévy stochastic differential equations 2020-05-23

Audio/music corpora Smells like Team Audioset 2014-08-08 – 2020-05-19

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

Empirical estimation of information Informing yourself from your data how informative your data was 2011-04-19 – 2020-04-28

Statistical relational learning 2020-04-26 – 2020-04-27

Mixture models for density estimation 2016-03-29 – 2020-04-24

Likelihood free inference 2020-04-22

Post stratification Making the optimal beverage from the fruit life gave you 2020-04-22

Analysis/resynthesis of audio 2016-01-15 – 2020-04-09

Queueing The mathematical field whose major result is enraging you about call centres 2015-06-03 – 2020-04-06

Epidemics 2020-03-10 – 2020-04-03

Model averaging On keeping many incorrect hypotheses and using them all as one goodish one 2017-06-20 – 2020-03-22

Order statistics 2019-02-21 – 2020-03-17

Effective sample size 2016-11-21 – 2020-03-03

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

Learnable indexes and hashes 2018-01-12 – 2020-02-18

Cepstral transforms and harmonic identification 2017-09-12 – 2020-02-13

R Shiny Statistics through the internet 2020-02-11

Branching processes 2014-08-18 – 2020-02-07

Digital forensics 2020-02-04 – 2020-02-07

Differential privacy 2016-07-28 – 2020-01-24

Learning in adaptive systems On staring into scopophilic abysses 2019-10-19 – 2020-01-22

Information geometry 2011-10-21 – 2019-12-27

Hawkes processes 2019-12-22

Spatial point process and their statistics 2016-08-17 – 2019-12-04

Non-uniform signal sampling Discrete sample representation of continuous signals without a grid 2019-01-08 – 2019-12-03

Audio source separation 2019-11-04 – 2019-11-26

Optimal control 2015-06-22 – 2019-11-01

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

Sparse regression 2016-06-23 – 2019-10-24

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

M-open, M-complete, M-closed 2016-05-30 – 2019-10-18

Density estimation Especially non- or semiparametrically 2016-06-06 – 2019-10-16

The tidyverse 2019-10-14

Non-negative matrix factorisation 2019-10-14

Statistical learning theory for time series 2016-11-03 – 2019-10-01

State filtering parameters Tracking things that don’t move 2017-09-15 – 2019-10-01

Probabilistic graphical models over continuous index sets 2014-08-05 – 2019-09-25

The interpretation of densities as intensities and vice versa Point process of observations ↔ observation of a point process 2016-09-13 – 2019-09-23

Correlograms Also covariances 2018-08-08 – 2019-09-22

Covariance estimation for stochastic processes Conversely, precision estimation 2014-11-16 – 2019-09-21

Defining dynamics via Gaussian processes 2019-09-18

Representer theorems 2019-09-16

Informations Entropies and other measures of surprise 2011-11-25 – 2019-09-10

Large sample theory 2015-02-15 – 2019-09-09

Biased sampling models Greasing non-squeaky wheels 2019-08-27

Bayesian model selection 2017-08-20 – 2019-07-22

Natural gradient descent Climbing slower on the tricky bits 2019-07-18

Fourier interpolation 2019-06-19

(Weighted) least squares fits 2016-09-22 – 2019-05-22

Statistics software 2015-02-28 – 2019-04-18

Ordinary differential equations Thou, silent form, dost tease us out of thought / As doth eternity 2019-03-28

Signal sampling Discrete representation of continuous signals and converse 2017-05-30 – 2019-03-08

Nearly sufficient statistics How about “Sufficient sufficiency”? — is that taken? 2018-03-13 – 2019-01-14

Bayesian sparsity 2019-01-08

Multiple testing 2015-04-22 – 2018-11-05

Sparse stochastic processes identification and sampling Discrete sample representation of sparse continuous stochastic processes 2018-11-22 – 2018-10-29

Hamiltonian and Langevin Monte Carlo Physics might be on to something 2018-07-31 – 2018-07-12

Signal processing That which you study for 4 years in order to design trippy music visualisers 2015-03-18 – 2018-01-05

Integral probability metrics 2016-08-21 – 2017-10-31

Relative age and dating Quantifying cradle-snatching 2017-10-14

Post-selection inference Adaptive data analysis without cheating 2017-08-20

Model/hyperparameter selection 2016-04-15 – 2017-08-20

Quantum-probabilistic graphical models 2017-08-07

State filtering for hidden Markov models Kalman and friends 2015-06-22 – 2017-07-06

Generating functions Fancy counting 2017-06-19

Marketing psychology 2017-04-27 – 2017-05-29

Granger causation/Transfer Entropy 2012-07-26 – 2017-05-04

Fractional Brownian motion 2017-02-18