# statistics

Visualising probabilistic graphical models
Also related models, such as Neural nets
2018-03-29
– 2023-02-02Ensemble Kalman methods
Data Assimilation; Data fusion; Sloppy filters for over-ambitious models
2015-06-22
– 2023-01-24Quantified self
Instrumentation and analytics for body and soul.
2022-01-11
– 2023-01-22Reparameterization tricks in inference
Pathwise gradient estimation, nNormalizing flows, invertible density models, inference by measure transport, low-dimensional coupling…
2018-04-04
– 2023-01-19Approximate matrix factorisation
Sometimes even exact
2014-08-05
– 2023-01-18Statistics and ML in python
2015-04-27
– 2023-01-18Optimal 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
– 2023-01-16Are they too old/young for me?
Dating, gender, age, and equity
2017-10-14
– 2023-01-15Maximum Mean Discrepancy
2016-08-21
– 2023-01-13Variational message-passing algorithms in graphical models
Cleaving reality at the joint, then summing it at the marginal
2014-11-25
– 2023-01-12Factor graphs
2019-12-16
– 2023-01-12Pytorch
#torched
2018-05-04
– 2023-01-12Goodhart’s Law
2019-12-22
– 2023-01-11(Kernelized) Stein variational gradient descent
KSVD, SVGD
2022-11-02
– 2023-01-09Probability divergences
Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses
2014-11-25
– 2023-01-06Transforms of Gaussian noise
Delta method, error propagation, unscented transform, Taylor expansion…
2014-11-25
– 2022-12-23Bayesian model selection by model evidence maximisation
Type II maximum likelihood, marginal maximum likelihood, Bayes Occam’s razor
2017-08-20
– 2022-12-22Deep sets
invariant and equivariant functions
2022-11-24
– 2022-12-08Mind as statistical learner
2020-06-23
– 2022-12-06Density ratio tricks
2022-12-06Statistical relational learning
2020-04-26
– 2022-11-22Adverse advice selection
2022-01-25
– 2022-11-15Hamiltonian and Langevin Monte Carlo
Physics might be on to something
2018-07-31
– 2022-11-14Explorables and interactives
Between exploratory data analysis and games
2020-03-12
– 2022-11-07Interaction effects and subgroups are probably what we want to estimate
2022-01-25
– 2022-11-06The edge of chaos
Computation, evolution, competition and other past-times of faculty
2016-12-01
– 2022-10-30Casual anthropic principles
Convenience-sampling lived human experience
2020-02-15
– 2022-10-28Bayesian sparsity
2019-01-08
– 2022-10-25Online learning
2018-09-30
– 2022-10-20Anomaly detection
I don’t define what is abnormal, but I know it when I see it
2015-10-06
– 2022-10-20Continuous and equilibrium probabilistic graphical models
2014-08-05
– 2022-10-10Data sets
Questions for answers looking for questions
2015-06-26
– 2022-10-08Forecasting
Vegan haruspicy
2015-06-16
– 2022-10-08Precision matrix estimation
Especially Gaussain
2014-11-16
– 2022-10-04(Weighted) least squares fits
2016-09-22
– 2022-10-04Multi level marketing
Pyramid schemes, Ponzi schemes, Newcomb unboxing
2022-10-02
– 2022-10-02Learning graphical models from data
Also, causal discovery, structure discovery
2017-09-20
– 2022-10-01Bayesian inverse problems in function space
a.k.a. Bayesian calibration, model uncertainty for PDEs and other wibbly, blobby things
2020-10-13
– 2022-09-26Neural denoising diffusion models
Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models…
2021-11-11
– 2022-09-24Conformal prediction
2016-12-26
– 2022-09-24The interpretation of RV densities as point process intensities and vice versa
Point process of observations ↔ observation of a point process
2016-09-13
– 2022-09-24Score matching
2021-11-11
– 2022-09-23Gaussian process inference by partial updates
2020-12-03
– 2022-09-22The Gaussian distribution
The default probability distribution
2016-06-27
– 2022-09-22Generalised Ornstein-Uhlenbeck processes
Markov/AR(1)-like processes
2022-01-10
– 2022-09-21Ensemble Kalman methods for training neural networks
Data assimilation for network weights
2022-09-20Penalised/regularised regression
2016-06-23
– 2022-09-19Laplace approximations in inference
Lightweight uncertainties, especially for heavy neural nets
2021-07-28
– 2022-09-06Causal inference in highly parameterized ML
2020-09-18
– 2022-09-02Gaussian belief propagation
Least squares at maximal elaboration
2014-11-25
– 2022-09-01Distributional robustness in inference
2019-07-12
– 2022-08-30Informations
Entropies and other measures of surprise
2011-11-25
– 2022-08-30Interoperating with R
2011-08-07
– 2022-08-25Elliptical belief propagation
Generalized least generalized squares
2022-08-22
– 2022-08-23The Matrix-Gaussian distribution
2022-08-19Bayes linear methods
some kind of approximate Bayes thing
2022-08-17
– 2022-08-18Recommender systems
2020-11-30
– 2022-08-15Data cleaning
90% of statistics
2020-01-22
– 2022-08-08Ablation studies, lesion studies, and complex systems
2016-10-26
– 2022-08-06Causal inference on DAGs
Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next
2016-10-26
– 2022-08-06Instumental 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
– 2022-08-06Hybrid machine/human ML
2021-09-13
– 2022-08-05ELBO
Evidence lower bound, variational free energy etc
2020-10-02
– 2022-08-03Data 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
– 2022-07-28Distances between Gaussian distributions
2016-06-27
– 2022-07-27Bayes linear regression and basis-functions in Gaussian process regression
a.k.a Fixed Rank Kriging, weight space GPs
2022-02-22
– 2022-07-27Integrated Nested Laplace Approximation
2021-07-28
– 2022-07-26Large sample theory
2015-02-15
– 2022-07-25Bayes for beginners
2016-05-30
– 2022-07-23Science 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
– 2022-07-05Inverse problems
2016-03-30
– 2022-06-30State filtering for hidden Markov models
Kalman and friends
2015-06-22
– 2022-06-28Stochastic signal sampling
Discrete sample representation of continuous stochastic processes
2017-05-30
– 2022-06-24Signal sampling
Discrete representation of continuous signals and converse
2017-05-30
– 2022-06-24Markov decision problems
2014-11-27
– 2022-06-07Machine learning and statistics in Julia
2019-11-27
– 2022-05-27Emergent spacetime
What are qubits again?
2022-05-26Applied psephology
2016-07-12
– 2022-05-25Spatial data in R
2021-03-21
– 2022-05-24GUIs for numerical array data
2015-03-04
– 2022-05-19Database and data file GUIs
2015-03-04
– 2022-05-19Nested sampling
2022-05-16Exponential families
2016-04-19
– 2022-05-13System 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
– 2022-05-04Expectation propagation
Generalized moment matching
2015-10-26
– 2022-05-04Forecasting with model averaging
Mixture of experts, ensembles and time series
2022-05-04Measure-valued stochastic processes
Moving masses
2020-10-16
– 2022-05-03Farming and husbandry of black swans and dragon kings
Heavy tailed and Knightian uncertainties for fun and profit
2020-09-22
– 2022-04-30Causal graphical model reading group 2022
Causal inference
2022-04-01
– 2022-04-29Generalized Bayesian Computation
2019-10-03
– 2022-04-28Inference without KL divergence
2019-10-03
– 2022-04-28SLAM
Simultaneous Location and Mapping
2014-11-25
– 2022-04-28Vecchia factoring of GP likelihoods
Ignore some conditioning in the dependencies and attain a sparse cholesky factor for the precision matrix
2022-04-27Hierarchical models
DAGs, multilevel models, random coefficient models, mixed effect models, structural equation models…
2015-06-07
– 2022-04-21Saying “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
– 2022-04-15Particle filters
incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names
2014-07-25
– 2022-04-10Particle 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
– 2022-04-07Predictive coding
Does the model that our brains do bayesian variational prediction make any actual predictions about our brains?
2011-11-27
– 2022-04-04Gumbel (soft) max tricks
Concrete distribution, relaxed categorical etc
2017-02-20
– 2022-04-01Change points
Looking for regime changes in stochastic processes. a.k.a. Switching state space models
2021-11-29
– 2022-04-01Pólya-Gamma augmentation trick
2017-02-20
– 2022-04-01Detecting stationarity in stochastic processes
Change-points, trends and transients
2021-11-29
– 2022-04-01Partition-valued random variates
2022-04-01Belief propagation
2014-11-25
– 2022-03-31Random binary vectors
The class of distributions that cause you to reinvent Shannon information if you stare at them long enough
2017-02-20
– 2022-03-30Measure-valued random variates
Including completely random measures and many generalizations
2020-10-16
– 2022-03-30Classification
Computer says no
2017-02-20
– 2022-03-28Generative flow
2022-03-07Survival analysis and reliability
Hazard rates, proportional hazard regression, life testing, mean time to failure
2019-03-12
– 2022-03-07Plotting in R
2019-10-13
– 2022-03-05Self-supervised learning
I just wanna be meeeeee / with high probabilityyy ♬♪
2022-03-04Gaussian processes on lattices
2019-10-30
– 2022-02-28Learning with conservation laws, invariances and symmetries
2020-04-11
– 2022-02-25Stability in dynamical systems
Lyapunov exponents and ilk
2019-05-21
– 2022-02-22M-estimation
2016-07-11
– 2022-02-17Politics as statistical learner
2022-01-31
– 2022-02-17System identification in continuous time
Learning in continuous ODEs, SDEs and CDEs
2016-08-01
– 2022-02-15Probabilistic programming
Doing statistics using the tools of computer science
2019-10-02
– 2022-02-11Variational inference
On fitting something not too far from a pretty good model that is not too hard
2016-03-22
– 2022-02-10Probabilistic graphical models
2014-08-05
– 2022-02-08Random graphical models
Causality in amongst confusion
2021-10-26
– 2022-02-07Karhunen-Loève expansions
2019-09-16
– 2022-02-01Spatial processes and statistics thereof
2011-07-29
– 2022-01-28Statistics and machine learning
2011-04-15
– 2022-01-27Feedback system identification, not necessarily linear
Learning dynamics from data
2016-08-01
– 2022-01-27Hypothesis tests, statistical
2014-08-23
– 2022-01-27Bootstrap
Shuffling reality to produce your data
2014-11-26
– 2022-01-27Teaching mathematics and statistics
2020-02-12
– 2022-01-27Learning on manifolds
Finding the lowest bit of a krazy straw, from the inside
2011-10-21
– 2022-01-26Psychometrics
Dimensionality reduction for souls
2017-10-31
– 2022-01-26Feedback system identification, linear
2016-07-27
– 2022-01-21(Outlier) robust statistics
2014-11-25
– 2022-01-21Risk perception and communication
2011-04-14
– 2022-01-17Bayesian inverse problems
2016-03-30
– 2022-01-13Categorical random variates
2017-02-20
– 2022-01-12Statistical projectivity
2020-04-26
– 2022-01-11Generalised autoregressive processes
2022-01-10Mind as statistical learner
2022-01-09R packaging, installation etc
2020-11-30
– 2022-01-07Matrix-valued random variates
2021-12-01
– 2022-01-06Garbled highlights from NeurIPS 2021
2021-11-05
– 2021-12-15R
The statistical programming language, not the letter
2011-08-07
– 2021-12-14Causality via potential outcomes
Neyman-Rubin, counterfactuals, conditional treatment effects, and related tricks
2016-10-26
– 2021-12-10Variational state filtering
2018-03-19
– 2021-12-08Convolutional subordinator processes
2021-03-08
– 2021-12-01Pyro
Approximate maximum in the density of probabilistic programming effort
2019-10-02
– 2021-11-25Combining kernels
2019-09-16
– 2021-11-25Gaussian Processes as stochastic differential equations
Imposing time on things
2019-09-18
– 2021-11-25Deep generative models
2020-12-10
– 2021-11-11External validity and dataset shift
Also transfer learning, learning under covariate shift, transferable learning, domain adaptation etc
2020-10-17
– 2021-11-03Probably Approximately Correct
2014-11-24
– 2021-10-27Survey modelling
Adjusting for the Lizardman constant
2019-08-29
– 2021-10-24Neural 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
– 2021-10-13Missing data
Imputation, estimation despite etc
2021-10-07Algorithmic statistics
Probably also algorithmic information theory
2014-07-25
– 2021-09-26Science for policy
Using evidence and reason to govern ourselves
2011-08-07
– 2021-09-22Fractals and self-similarity
2011-11-13
– 2021-09-22Approximate Bayesian Computation
Posterior updates without likelihood
2020-08-25
– 2021-09-20Heavy tails
Weird things about rare massive events
2020-01-13
– 2021-09-18Fractional differential equations
2016-03-22
– 2021-09-13VS Code as R IDE
2021-10-06
– 2021-08-22Convolutional stochastic processes
Moving averages of noise
2021-03-01
– 2021-08-16Rough path theory
Also, signatures.
2021-04-02
– 2021-08-05Generalised linear models
2016-03-24
– 2021-08-05Models for count data
2015-05-14
– 2021-08-03Publication bias
Replication crises, P-values, bitching about journals, other debilities of contemporary science at large
2016-08-30
– 2021-07-22IDEs for R
Friendly UIs for the almost-friendly statistical programming language
2011-08-07
– 2021-07-18Contagion processes and their statistics
2016-08-30
– 2021-07-15Media virality
Strategic modelling for content creators
2016-08-30
– 2021-07-15Learning summary statistics
2020-04-22
– 2021-07-15Tensorflow
The framework to use for deep learning if you groupthink like Google
2016-07-11
– 2021-07-07Graph sampling
Estimating functionals of graphs
2020-02-15
– 2021-07-06Uncertainty quantification
2016-12-26
– 2021-07-06Semi/weakly-supervised learning
On extracting nutrition from bullshit
2017-07-24
– 2021-07-05Extreme value theory
On the decay of awfulness with oftenness
2020-01-13
– 2021-06-30Learning on tabular data
2020-11-30
– 2021-06-21Optimal transport metrics
Wasserstein distances, Monge-Kantorovich metrics, Earthmover distances
2019-05-30
– 2021-06-08Energy based models
Inference with kinda-tractable un-normalized densities
2021-06-07Stein’s method
His eyes are like angels but his heart is cold / No need to ask / He’s a Stein operator
2021-03-12
– 2021-06-01Neural net kernels
2019-09-16
– 2021-05-24Cross validation
2016-09-05
– 2021-05-13Spectral factorization
2021-05-05
– 2021-05-07Wiener-Hopf method
Righteous hack for certain integral equations
2021-05-05ML benchmarks and their pitfalls
On marginal efficiency gain in paperclip manufacture
2020-08-16
– 2021-04-13Prediction processes
Some kind of weird time series formalism
2021-04-09Dynamical systems via Koopman operators
Composition operators, Dynamic Extended Mode decompositions…
2020-10-13
– 2021-04-09Kernel zoo
2019-09-16
– 2021-03-30Generically approximating probability distributions
2021-03-12
– 2021-03-22Statistics, computational complexity thereof
2020-11-03
– 2021-03-10Mind reading by computer
The ultimate inverse problem
2017-07-01
– 2021-03-03Convolutional Gaussian processes
2021-03-01Random fields as stochastic differential equations
Precision vs covariance, fight!
2020-10-12
– 2021-03-01Stochastic processes on manifolds
2021-03-01Learning covariance functions
Learning a family of covariances at once
2019-09-16
– 2021-03-01Stability in linear dynamical systems
This Bodes well
2019-07-19
– 2021-02-16Feynman-Kac formulae
2021-01-27Miscellaneous nonstationary kernels
2019-09-16
– 2021-01-21Warping of stationary stochastic processes
2019-09-16
– 2021-01-21Bayesians vs frequentists
Just because we both get the same answer doesn’t mean neither of us is wrong
2014-11-25
– 2021-01-14Statistical mechanics of statistics
2016-12-01
– 2021-01-06Covariance functions
Variograms, Mercer kernels, positive definite operators, spare reproducing kernels for that Hilbert space I bought on eBay real cheap
2019-09-16
– 2021-01-05Garbled highlights from NeurIPS 2020
2020-09-17
– 2020-12-11Random embeddings and hashing
2016-12-05
– 2020-12-01Randomised regression
2017-01-13
– 2020-12-01Distribution regression
2020-12-01Probabilistic spectral analysis
2019-11-13
– 2020-11-25Observability and sensitivity in learning dynamical systems
Parameter identifiability in dynamical models
2020-11-09Weighted data in statistics
2020-11-04
– 2020-11-06Efficient factoring of GP likelihoods
2020-10-16
– 2020-10-26Stan
The flagship Bayesian workhorse
2020-10-19Sparse model selection
2016-09-05
– 2020-10-02Quantitative risk measurement
Mathematics of actuarial and financial disaster
2015-04-30
– 2020-09-22Filter design, linear
Especially digital
2017-07-24
– 2020-09-18Data summarization
On maps drawn at smaller than 1:1 scale
2019-01-14
– 2020-09-18Independence, conditional, statistical
2016-04-21
– 2020-09-13Statistics of spatio-temporal processes
2020-09-11
– 2020-09-11Statistics of spatio-temporal processes
2020-09-11
– 2020-09-11Dimensionality 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
– 2020-09-11Variational autoencoders
2019-11-04
– 2020-09-10Causal graphical model reading group 2020
An introduction to conditional independence DAGs and their use for causal inference.
2020-08-30
– 2020-09-03Causal Bayesian networks
Staged tree models, probability trees …Causalan Bayesian networks
2020-11-01
– 2020-09-01Minimum description length
2020-08-06Plotting stuff in julia
2019-05-31
– 2020-07-25Learning of manifolds
Also topological data analysis; other hip names to follow
2014-08-19
– 2020-06-23Model complexity penalties
Information criteria, degrees of freedom etc
2015-04-22
– 2020-06-22Long memory time series
2011-11-13
– 2020-05-28Natural gradient descent
Climbing slower on the tricky bits
2019-07-18
– 2020-05-26Variational inference
On fitting the best model one can be bothered to
2016-03-22
– 2020-05-24Lévy stochastic differential equations
2020-05-23Audio/music corpora
Smells like Team Audioset
2014-08-08
– 2020-05-19Directed graphical models
2017-09-20
– 2020-05-13Empirical estimation of information
Informing yourself from your data how informative your data was
2011-04-19
– 2020-04-28Mixture models for density estimation
2016-03-29
– 2020-04-24Likelihood free inference
2020-04-22Matching and weighting
Making the optimal beverage from the fruit life gave you
2020-04-22Analysis/resynthesis of audio
2016-01-15
– 2020-04-09Queueing
The mathematical field whose major result is enraging you about call centres
2015-06-03
– 2020-04-06Epidemics
2020-03-10
– 2020-04-03Model averaging
On keeping many incorrect hypotheses and using them all as one goodish one
2017-06-20
– 2020-03-22Order statistics
2019-02-21
– 2020-03-17Effective sample size
2016-11-21
– 2020-03-03Bias reduction
Estimating the bias of an estimator so as to subtract it off again
2020-02-26Learnable indexes and hashes
2018-01-12
– 2020-02-18Cepstral transforms and harmonic identification
2017-09-12
– 2020-02-13R Shiny
Statistics through the internet
2020-02-11Branching processes
2014-08-18
– 2020-02-07Digital forensics
2020-02-04
– 2020-02-07Differential privacy
2016-07-28
– 2020-01-24Learning in adaptive systems
On staring into scopophilic abysses
2019-10-19
– 2020-01-22Information geometry
2011-10-21
– 2019-12-27Hawkes processes
2019-12-22Spatial point process and their statistics
2016-08-17
– 2019-12-04Non-uniform signal sampling
Discrete sample representation of continuous signals without a grid
2019-01-08
– 2019-12-03Audio source separation
2019-11-04
– 2019-11-26Optimal control
2015-06-22
– 2019-11-01Undirected graphical models
2017-09-20
– 2019-10-28Sparse regression
2016-06-23
– 2019-10-24Frequentist consistency of Bayesian methods
TFW two flawed methods for understanding the world agree with at least each other
2016-04-12
– 2019-10-19M-open, M-complete, M-closed
2016-05-30
– 2019-10-18Density estimation
Especially non- or semiparametrically
2016-06-06
– 2019-10-16The tidyverse
2019-10-14Non-negative matrix factorisation
2019-10-14Statistical learning theory for time series
2016-11-03
– 2019-10-01State filtering parameters
Tracking things that don’t move
2017-09-15
– 2019-10-01Correlograms
Also covariances
2018-08-08
– 2019-09-22Covariance matrix estimation
Esp Gaussian
2014-11-16
– 2019-09-21Defining dynamics via Gaussian processes
2019-09-18Representer theorems
2019-09-16Biased sampling models
Greasing non-squeaky wheels
2019-08-27Bayesian model selection
2017-08-20
– 2019-07-22Fourier interpolation
2019-06-19Statistics software
2015-02-28
– 2019-04-18Ordinary differential equations
Thou, silent form, dost tease us out of thought / As doth eternity
2019-03-28Nearly sufficient statistics
How about “Sufficient sufficiency”? — is that taken?
2018-03-13
– 2019-01-14Multiple testing
2015-04-22
– 2018-11-05Sparse stochastic processes identification and sampling
Discrete sample representation of sparse continuous stochastic processes
2018-11-22
– 2018-10-29Signal processing
That which you study for 4 years in order to design trippy music visualisers
2015-03-18
– 2018-01-05Post-selection inference
Adaptive data analysis without cheating
2017-08-20Model/hyperparameter selection
2016-04-15
– 2017-08-20Quantum-probabilistic graphical models
2017-08-07Generating functions
Fancy counting
2017-06-19Marketing psychology
2017-04-27
– 2017-05-29Granger causation/Transfer Entropy
2012-07-26
– 2017-05-04Fractional Brownian motion
2017-02-18Metric entropy
2017-02-13Garbled highlights from NIPS 2016
2016-12-05
– 2017-02-03Special functions
2014-07-25
– 2016-12-21The simplex
2016-10-25UNSW Stats reading group 2016 - Causal DAGs
An introduction to conditional independence DAGs and their use for causal data.
2016-10-17
– 2016-10-21Inference from disorder
2016-10-19Maximum likelihood inference
2015-02-15
– 2016-10-13Stability (in learning)
2016-05-25
– 2016-10-05Kernel density estimators
2016-03-05
– 2016-08-18Blind deconvolution
2015-03-01
– 2016-07-27“Approximate models”
2016-07-04Curved exponential families
2016-04-19Expectation maximisation
2014-08-17
– 2016-04-17Deconvolution
2015-04-19
– 2016-04-11Indirect inference
2014-12-23
– 2015-12-15Count time series models
2015-06-03
– 2015-12-09High frequency time series estimation
2016-06-12
– 2015-12-02Copula functions
2015-06-23Elliptical distributions
2015-06-23Complexity
2011-11-25
– 2015-04-11Computational mechanics
2011-10-17
– 2015-01-02