# probability

Neural denoising diffusion models
Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models…
2021-11-11
– 2022-09-24Ensemble Kalman methods
Data Assimilation; Data fusion; Sloppy filters for over-ambitious models
2015-06-22
– 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
– 2022-09-23The Gaussian distribution
The default probability distribution
2016-06-27
– 2022-09-22Generalised Ornstein-Uhlenbeck processes
Markov/AR(1)-like processes
2022-01-10
– 2022-09-21Bayesian posterior sampling via SGD
One of those times when the easy thing can also be the smart thing
2020-08-17
– 2022-09-21Langevin dynamcs MCMC
2020-08-17
– 2022-09-21Conditional expectation and probability
2020-02-04
– 2022-09-21Ensemble Kalman methods for training neural networks
Data assimilation for network weights
2022-09-20Penalised/regularised regression
2016-06-23
– 2022-09-19Factor graphs
2019-12-16
– 2022-09-06Laplace approximations in inference
Lightweight uncertainties, especially for heavy neural nets
2021-07-28
– 2022-09-06Adverse advice selection
2022-01-25
– 2022-09-03Causal inference in highly parameterized ML
2020-09-18
– 2022-09-02Learning graphical models from data
Also, causal discovery, structure discovery
2017-09-20
– 2022-09-02Transforms of Gaussian noise
Delta method, error propagation, unscented transform, Taylor expansion…
2014-11-25
– 2022-09-01Gaussian belief propagation
Least squares at maximal elaboration
2014-11-25
– 2022-09-01Distributional robustness in inference
2019-07-12
– 2022-08-30Probability divergences
Metrics, contrasts and divergences and other ways of quantifying how similar are two randomnesses
2014-11-25
– 2022-08-30Generic variance reduction in Monte Carlo samplers
2022-08-25Elliptical belief propagation
Generalized least generalized squares
2022-08-22
– 2022-08-23The Matrix-Gaussian distribution
2022-08-19Monte Carlo gradient estimation
Especially stochastic automatic differentiation
2020-09-30
– 2022-08-18Recommender systems
2020-11-30
– 2022-08-15Ablation 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
– 2022-08-06ELBO
Evidence lower bound, variational free energy etc
2020-10-02
– 2022-08-03Distances between Gaussian distributions
2016-06-27
– 2022-07-27Gamma distributions
2019-10-14
– 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
– 2022-07-25Mind as statistical learner
2020-06-23
– 2022-07-25Interaction effects and subgroups are probably what we want to estimate
2022-01-25
– 2022-07-21Empirical mode decomposition
Multiplying your exposure to uncertainty principles
2018-01-06
– 2022-07-12Inverse problems
2016-03-30
– 2022-06-30Approximate matrix factorisation
Sometimes even exact
2014-08-05
– 2022-06-29State filtering for hidden Markov models
Kalman and friends
2015-06-22
– 2022-06-28Betting
2017-05-09
– 2022-06-27Bayesian inverse problems in function space
a.k.a. Bayesian calibration, model uncertainty for PDEs and other wibbly, blobby things
2020-10-13
– 2022-06-24Markov Chain Monte Carlo methods
2017-08-28
– 2022-06-08Markov decision problems
2014-11-27
– 2022-06-07Emergent spacetime
What are qubits again?
2022-05-26Stochastic partial differential equations
SDEs taking values in some function space
2021-01-27
– 2022-05-25Nested 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
– 2022-05-04Casual anthropic principles
Convenience-sampling lived human experience
2020-02-15
– 2022-05-01Farming 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
2022-04-27Anomaly detection
I don’t define what is abnormal, but I know it when I see it
2015-10-06
– 2022-04-21Hierarchical models
DAGs, multilevel models, random coefficient models, mixed effect models, structural equation models…
2015-06-07
– 2022-04-21Markov bridge processes
Especially Lévy bridges
2019-10-15
– 2022-04-20Divisible, decomposable and stable distributions
Ways of slicing randomness
2017-06-15
– 2022-04-18Particle filters
incorporating Interacting Particle Systems, Sequential Monte Carlo and a profusion of other simultaneous-discovery names
2014-07-25
– 2022-04-10Stationary Gamma processes
2019-10-14
– 2022-04-08Particle 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
– 2022-04-08Predictive coding
Does the model that our brains do bayesian variational prediction make any actual predictions about our brains?
2011-11-27
– 2022-04-04Beta and Dirichlet distributions
2019-10-14
– 2022-04-04Gumbel (soft) max tricks
Concrete distribution, relaxed categorical etc
2017-02-20
– 2022-04-01Pólya-Gamma augmentation trick
2017-02-20
– 2022-04-01Partition-valued random variates
2022-04-01Belief propagation
2014-11-25
– 2022-03-31Measure-valued random variates
Including completely random measures and many generalizations
2020-10-16
– 2022-03-30Multivariate Gamma distributions
2019-10-14
– 2022-03-14Generative flow
2022-03-07Survival analysis and reliability
Hazard rates, proportional hazard regression, life testing, mean time to failure
2019-03-12
– 2022-03-07Fun with rotational symmetries
2021-01-29
– 2022-03-03Matrix- and vector-valued generalizations of Gamma processes
2019-10-14
– 2022-03-03Lévy Gamma processes
2019-10-14
– 2022-03-03Gaussian processes on lattices
2019-10-30
– 2022-02-28Learning with conservation laws, invariances and symmetries
2020-04-11
– 2022-02-25Subordinators
Non-decreasing Lévy processes with weird branding
2019-10-14
– 2022-02-24Stability in dynamical systems
Lyapunov exponents and ilk
2019-05-21
– 2022-02-22Visualising probabilistic graphical models
Also related models, such as Neural nets
2018-03-29
– 2022-02-18Message-passing algorithms in graphical models
Cleaving reality at the joint, then summing it at the marginal
2014-11-25
– 2022-02-17Politics as statistical learner
2022-01-31
– 2022-02-17Risk perception and communication
2011-04-14
– 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
– 2022-02-07Statistics and machine learning
2011-04-15
– 2022-01-27Risk perception and communication
2011-04-14
– 2022-01-17Bayesian inverse problems
2016-03-30
– 2022-01-13Gamma-Beta algebra
2019-10-14
– 2022-01-12Categorical random variates
2017-02-20
– 2022-01-12Statistical projectivity
2020-04-26
– 2022-01-11Generalised autoregressive processes
2022-01-10Mind as statistical learner
2022-01-09Matrix-valued random variates
2021-12-01
– 2022-01-06Reparameterization tricks in inference
Pathwise gradient estimation, nNormalizing flows, invertible density models, inference by measure transport, low-dimensional coupling…
2018-04-04
– 2021-12-21Time frequency analysis
Multiplying your exposure to uncertainty principles
2018-01-06
– 2021-12-21Causality via potential outcomes
Neyman-Rubin, counterfactuals, conditional treatment effects, and related tricks
2016-10-26
– 2021-12-10Variational state filtering
2018-03-19
– 2021-12-08Random rotations
2021-05-18
– 2021-12-01Gaussian Processes as stochastic differential equations
Imposing time on things
2019-09-18
– 2021-11-25Trading, hedging and portfolios in practice
2017-05-09
– 2021-11-18Lévy processes
2017-05-29
– 2021-11-17Deep 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
– 2021-11-03High dimensional statistics
2015-03-12
– 2021-10-28Neural music synthesis
2016-01-15
– 2021-10-14Missing data
Imputation, estimation despite etc
2021-10-07Science for policy
Using evidence and reason to govern ourselves
2011-08-07
– 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
– 2021-09-18Monte Carlo methods
2014-11-16
– 2021-09-02Path smoothness properties of stochastic processes
Continuity, differentiability and other smoothness properties
2020-02-26
– 2021-09-02Stochastic calculus
Itô and friends
2019-09-19
– 2021-08-31Risk neutral measure
2021-10-15
– 2021-08-08Cascade models
a.k.a. cluster distributions, Galton-Watson models
2019-10-11
– 2021-08-04Contagion 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
– 2021-07-15Graph sampling
Estimating functionals of graphs
2020-02-15
– 2021-07-06Extreme value theory
On the decay of awfulness with oftenness
2020-01-13
– 2021-06-30Algebraic probability
If you liked it then you prob’ly put a ring on it
2017-06-15
– 2021-06-24Backward stochastic differential equations
2019-09-19
– 2021-06-22Stochastic differential equations
2019-09-19
– 2021-06-22Learning 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
2021-03-12
– 2021-06-01Isotropic random vectors
2011-08-10
– 2021-05-24Randomized low dimensional projections
2021-03-12
– 2021-05-24Transforms of random variates
2020-06-04
– 2021-05-14Stochastic Taylor expansion
Polynomial approximations of small randomnesses
2020-10-15
– 2021-05-14Cross validation
2016-09-05
– 2021-05-13Spectral factorization
2021-05-05
– 2021-05-07Wiener-Hopf method
Righteous hack for certain integral equations
2021-05-05Path integral formulations
Feynman path integrals, esp for stochastic processes
2021-04-19Prediction processes
Some kind of weird time series formalism
2021-04-09Generically approximating probability distributions
2021-03-12
– 2021-03-22Log concave distributions
associated tools
2017-08-28
– 2021-03-11Matrix measure concentration inequalities and bounds
2014-11-25
– 2021-03-08Measure concentration inequalities
On being 80% sure I am only 20% wrong
2014-11-25
– 2021-03-04Random fields as stochastic differential equations
Precision vs covariance, fight!
2020-10-12
– 2021-03-01Frames and Riesz bases
Generalisations of orthogonal bases
2017-06-12
– 2021-02-24Causal inference in the continuous limit
2021-02-17Stability in linear dynamical systems
This Bodes well
2019-07-19
– 2021-02-16Chaos expansions
Polynomial chaos, generalized polynomial chaos, arbitrary chaos etc
2020-05-21
– 2021-02-15Feynman-Kac formulae
2021-01-27Generative adversarial networks
2016-10-07
– 2020-12-14Random embeddings and hashing
2016-12-05
– 2020-12-01Randomised regression
2017-01-13
– 2020-12-01Probabilistic spectral analysis
2019-11-13
– 2020-11-25Weighted data in statistics
2020-11-04
– 2020-11-06Markov Chain Monte Carlo methods
2020-06-28
– 2020-10-28Efficient factoring of GP likelihoods
2020-10-16
– 2020-10-26Sparse model selection
2016-09-05
– 2020-10-02Monte Carlo optimisation
2020-09-30Splitting simulation
2017-05-29
– 2020-09-28Quantitative risk measurement
Mathematics of actuarial and financial disaster
2015-04-30
– 2020-09-22Data summarization
On maps drawn at smaller than 1:1 scale
2019-01-14
– 2020-09-18Independence, conditional, statistical
2016-04-21
– 2020-09-13Dimensionality 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
– 2020-09-01Online learning
2018-09-30
– 2020-08-26Variational inference
On fitting the best model one can be bothered to
2016-03-22
– 2020-05-24Directed graphical models
2017-09-20
– 2020-05-13Adaptive Markov Chain Monte Carlo samplers
2020-04-28
– 2020-04-30Tuning an MCMC sampler
2020-04-30
– 2020-04-30The cross entropy method
2020-04-24Mixture models for density estimation
2016-03-29
– 2020-04-24Likelihood free inference
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
– 2020-04-06Order statistics
2019-02-21
– 2020-03-17Restricted isometry properties
Plus incoherence, irrepresentability, and other uncertainty bounds for a sparse world, and maybe frame theory, what’s that now?
2017-06-12
– 2020-03-09Kernel approximation
2016-07-27
– 2020-03-06Potential theory in probability
Something about harmonic functions or whatever
2020-02-12Random change of time
Stochastic processes derived by varying the rate of time’s passage, which is more convenient than I imagined
2015-08-05
– 2020-02-10Branching processes
2014-08-18
– 2020-02-07Infinitesimal generators
Generators of the transition semi-group, connection to Kolmogorov forward equations
2017-05-29
– 2020-02-05Poisson processes
2019-10-14
– 2020-01-29Hawkes processes
2019-12-22Convergence of random variables
2019-12-03Cherchez la martingale
Stuff about probability and orthogonality
2019-11-25
– 2019-11-30Audio 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
– 2019-10-24Density estimation
Especially non- or semiparametrically
2016-06-06
– 2019-10-16Non-negative matrix factorisation
2019-10-14Zeros of random trigonometric polynomials
2019-05-20
– 2019-10-14Generalized Galton-Watson processes
2014-12-28
– 2019-10-11Random (element) matrix theory
2014-11-09
– 2019-10-10State filtering parameters
Tracking things that don’t move
2017-09-15
– 2019-10-01Probabilistic graphical models over continuous index sets
2014-08-05
– 2019-09-25Defining dynamics via Gaussian processes
2019-09-18Probability
2011-07-29
– 2019-07-15Fourier interpolation
2019-06-19Nearly sufficient statistics
How about “Sufficient sufficiency”? — is that taken?
2018-03-13
– 2019-01-14Rare-event-conditional estimation
2017-05-29
– 2017-11-10Quantum probability
and quantum information, noncommutative probability
2016-10-16
– 2017-09-18Quantum-probabilistic graphical models
2017-08-07Warping and registration problems
Matching up of bumps and wibbles in stretchy things
2016-08-17
– 2017-07-23Generating functions
Fancy counting
2017-06-19Stochastic processes and fields
Probabilistic structures over index sets and state spaces
2014-08-05
– 2017-06-15Compressed sensing and sampling
A fancy ways of counting zero
2014-08-18
– 2017-06-14Marketing psychology
2017-04-27
– 2017-05-29Uncertainty principles
2017-05-14Fractional Brownian motion
2017-02-18Metric entropy
2017-02-13Special 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
– 2016-10-13Statistical learning theory
Eventually including structural risk minimisation, risk bounds, hopefully-uniform convergence rates, VC-dimension, generalisation-and-stability framings etc
2016-07-06
– 2016-08-16Randomised linear algebra
2016-08-16Change of probability measure
2015-08-05
– 2016-08-16Blind deconvolution
2015-03-01
– 2016-07-27Maximum processes
2016-07-14Curved 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
– 2015-12-09Hidden variable formalisms in quantum mechanics
2015-11-28Random number generation
2015-05-14
– 2015-10-13Copula functions
2015-06-23Elliptical distributions
2015-06-23Sigma algebras, probability spaces, measure theory
The scaffolding of randomness
2015-06-20