# statistics

Survey modelling
Adjusting for the Lizardman constant
2019-08-29
– 2021-10-24
Anthropic principles
Convenience-sampling lived human experience
2020-02-15
– 2021-10-24
Data dashboards
On assuring the client that you are doing something data-sciency because it looks like in the movies
2020-03-12
– 2021-10-19
Pytorch
#torched
2018-05-04
– 2021-10-16
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
Psychometrics
Dimensionality reduction for souls
2017-10-31
– 2021-10-02
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
Bootstrap
Shuffling reality to produce your data
2014-11-26
– 2021-09-18
Heavy tails
Weird things about rare massive events
2020-01-13
– 2021-09-18
Databases viewers / editors
2015-03-04
– 2021-09-15
Fractional differential equations
2016-03-22
– 2021-09-13
Teaching mathematics and statistics
With a side order of communicating science
2020-02-12
– 2021-09-08
Forecasting
Haruspicy 2.0
2015-06-16
– 2021-09-06
Diagramming and visualising graphical models and neural networks
My need for this is conditionally dependent upon my deadline, given the subject matter
2018-03-29
– 2021-09-06
Karhunen-Loève expansions
2019-09-16
– 2021-08-27
Inverse problems
2016-03-30
– 2021-08-23
VS Code as R IDE
2021-10-06
– 2021-08-22
R
The statistical programming language, not the letter
2011-08-07
– 2021-08-20
Causal inference in highly parameterized ML
2020-09-18
– 2021-08-17
External validity
Transfer learning, dataset shift, learning under covariate shift, transferable learning, domain adaptation etc
2020-10-17
– 2021-08-16
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
Feedback system identification, not necessarily linear
2016-08-01
– 2021-08-04
Tests, statistical
Maybe also design of experiments while we are here?
2014-08-23
– 2021-08-04
Models for count data
2015-05-14
– 2021-08-03
Spatial data in R
2021-03-21
– 2021-07-29
Data cleaning
90% of statistics
2020-01-22
– 2021-07-29
Laplace approximations in inference
Posterior updates without likelihood
2021-07-28
Risk perception and communication
2011-04-14
– 2021-07-27
Publication bias
Replication crises, P-values, bitching about journals, other debilities of contemporary science at large
2016-08-30
– 2021-07-22
Recommender systems
2020-11-30
– 2021-07-19
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
Predictive coding
Fancy analogy for brains
2011-11-27
– 2021-06-24
Causality via potential outcomes
Neyman-Rubin, counterfactuals, instrumental variables and related tricks
2016-10-26
– 2021-06-21
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
Deep generative models
2020-12-10
– 2021-06-08
Energy based models
Inference with kinda-tractable un-normalized densities
2021-06-07
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
Stein’s method
2021-03-12
– 2021-06-01
Neural net kernels
2019-09-16
– 2021-05-24
Gaussian Process regression via state filtering
Imposing time on things
2019-09-18
– 2021-05-21
Causal inference on DAGs
Confounding! This scientist performed miracle graph surgery during an intervention and you won’t believe what happened next
2016-10-26
– 2021-05-19
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
Inference without KL divergence
2019-10-03
– 2021-04-23
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
Statistics and machine learning
2011-04-15
– 2021-04-08
Kernel zoo
2019-09-16
– 2021-03-30
Generically approximating probability distributions
2021-03-12
– 2021-03-22
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
Probabilistic programming
Programming with probability distributions for, e.g. Bayesian inference
2019-10-02
– 2021-03-10
Statistics, computational complexity thereof
2020-11-03
– 2021-03-10
Classification
Computer says no
2017-02-20
– 2021-03-09
Reparameterization tricks in inference
Normalizing flows, invertible density models, inference by measure transport, low-dimensional coupling…
2018-04-04
– 2021-03-08
Stochastic processes which represent measures over the reals
2020-10-16
– 2021-03-08
Convolutional subordinator processes
2021-03-08
Learning on manifolds
Finding the lowest bit of a krazy straw, from the inside
2011-10-21
– 2021-03-03
Mind reading by computer
The ultimate inverse problem
2017-07-01
– 2021-03-03
Combining kernels
2019-09-16
– 2021-03-01
Convolutional Gaussian processes
2021-03-01
Random fields as stochastic differential equations
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
R packaging, installation etc
2020-11-30
Variational inference by message-passing in graphical models
2014-11-25
– 2020-11-25
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
Differentiating through the Gamma
2020-06-12
– 2020-10-15
Inverse problems for complex models
a.k.a. Bayesian calibration, model uncertainty
2020-10-13
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 …Causlan Bayesian networks
2020-11-01
– 2020-09-01
Statistics and ML in python
2015-04-27
– 2020-08-27
Online learning
2018-09-30
– 2020-08-26
Plotting in R
2019-10-13
– 2020-08-26
Minimum description length
2020-08-06
Plotting stuff in julia
2019-05-31
– 2020-07-25
(Outlier) robust statistics
2014-11-25
– 2020-07-14
(Approximate) matrix factorisation
2014-08-05
– 2020-07-03
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
Natural gradient descent
Climbing slower on the tricky bits
2019-07-18
– 2020-05-26
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
Learning with conservation laws, invariances and symmetries
2020-04-11
– 2020-05-01
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
Statistical projectivity
2020-04-26
Mixture models for density estimation
2016-03-29
– 2020-04-24
Likelihood free inference
2020-04-22
Post stratification
Making the optimal lemonade from the fruit life gave you
2020-04-22
Learning graphical models from data
What is independent of what?
2017-09-20
– 2020-04-11
Analysis/resynthesis of audio
2016-01-15
– 2020-04-09
Particle filters
incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names
2014-07-25
– 2020-04-08
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
Survival analysis and reliability
Hazard rates, proportional hazard regression, life testing, mean time to failure
2019-03-12
– 2020-02-05
Bayes for beginners
2016-05-30
– 2020-01-31
Differential privacy
2016-07-28
– 2020-01-24
Learning in adaptive systems
On staring into scopophilic abysses
2019-10-19
– 2020-01-22
Applied psephology
2016-07-12
– 2020-01-20
Information geometry
2011-10-21
– 2019-12-27
Hawkes processes
2019-12-22
Factor graphs
2019-12-16
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
Gaussian processes on lattices
2019-10-30
Inference on graphical models
Given what I know about what I know, what do I know?
2017-09-20
– 2019-10-28
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
Exponential families
2016-04-19
– 2019-10-12
Spatial processes and statistics thereof
2011-07-29
– 2019-10-03
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
2014-11-16
– 2019-09-21
Defining dynamics via Gaussian processes
2019-09-18
Representer theorems
2019-09-16
Large sample theory
2015-02-15
– 2019-09-09
Biased sampling models
Greasing non-squeaky wheels
2019-08-27
Hierarchical models
DAGs, multilevel models, random coefficient models, mixed effect models…
2015-06-07
– 2019-08-19
Bayesian model selection
2017-08-20
– 2019-07-22
Fourier interpolation
2019-06-19
(Weighted) least squares fits
2016-09-22
– 2019-05-22
Stability in dynamical systems
Lyapunov exponents and ilk
2019-05-21
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
Variational state filtering
2018-03-19
– 2018-12-07
Multiple testing
2015-04-22
– 2018-11-05
Sparse stochastic processes and sampling
Discrete sample representation of sparse continuous stochastic processes
2018-11-22
– 2018-10-29
Feedback system identification, linear
2016-07-27
– 2018-10-23
Anomaly detection
I don’t define what is normal, but I know it when I see it
2015-10-06
– 2018-10-22
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
Probabilistic graphical models
2014-08-05
– 2017-09-11
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
Metric entropy
2017-02-13
Garbled highlights from NIPS 2016
2016-12-05
– 2017-02-03
Special functions
2014-07-25
– 2016-12-21
UNSW Stats reading group 2016 - Causal DAGs
An introduction to conditional independence DAGs and their use for causal data.
2016-10-17
– 2016-10-21
Inference from disorder
2016-10-19
Maximum likelihood inference
2015-02-15
– 2016-10-13
M-estimation
2016-07-11
– 2016-10-10
Stability (in learning)
2016-05-25
– 2016-10-05
Penalised/regularised regression
2016-06-23
– 2016-09-15
Kernel density estimators
2016-03-05
– 2016-08-18
Blind deconvolution
2015-03-01
– 2016-07-27
“Approximate models”
2016-07-04
Probably Approximately Correct
2014-11-24
– 2016-05-29
Curved exponential families
2016-04-19
Expectation maximisation
2014-08-17
– 2016-04-17
Deconvolution
2015-04-19
– 2016-04-11
Indirect inference
2014-12-23
– 2015-12-15
Count time series models
2015-06-03
– 2015-12-09
High frequency time series estimation
2016-06-12
– 2015-12-02
Expectation propagation
2015-10-26
Copula functions
2015-06-23
Elliptical distributions
2015-06-23
Complexity
2011-11-25
– 2015-04-11
Computational mechanics
2011-10-17
– 2015-01-02