probabilistic inference
Books
Papers
Lectures
People
Methods
Software libraries
- ☐ Complete next element of tutorial @est(1h) @today
- ☐ Deep HMM tutorial referenced in David Duvenaud’s talks on learning stochastic differential equations
Gen
- DONE Complete ground up intro @est(1h) @done(19-11-23 01:27)
- DONE Test regression example @est(1h) @done(19-11-23 02:35)
- TODO Translate some other model @est(2h)
Paysage:
- Read section I of high-bias low-variance introduction to ML
Links to this note
- open documents
- linear models
- Towards A Principled Bayesian Workflow by Michael Betancourt in 2020
- Data analysis using regression and multilevel hierarchical models by Andrew Gelman and Jennifer Hill in 2006
- Bayesian data analysis by Andrew Gelman, Aki Vehtari, et al in 2014
- Applied Probabilistic Modeling Principled Bayesian Inference for the Discerning Practitioner by Michael Betancourt in 2020
- approximate bayesian computation
- The information geometry of 2-field functional integrals by Eric Smith in 2019
- The Bayesian brain the role of uncertainty in neural coding and computation by David C.Knill and Alexandre Pouget in 2004
- Probabilistic Modeling and Statistical Inference by Michael Betancourt in 2019
- Bayesian probabilistic inference for stochastic processes
- Bayesian informal logic and fallacy by Kevin Korb in 2003
- Bayesian approaches to clinical trials and health-care evaluation by David Spiegelhalter, Keith Abrams, and Jonathan Myles in 2004
- Applications of probabilistic inference to the interpretation of simple experiments
- Aki Vehtari
- A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data by Dimitrios Vavoulis et al in 2020
- free energy principle
- The free energy principle for action and perception A mathematical review by Christopher Buckley, Anil Seth, et al in 2017
- variational inference
- How does monad-bayes compare to pyro?
- Bayesian brain hypothesis
- variational autoencoder
- turing.jl
- pyprob
- pyro
- monad-bayes
- model construction
- metacademy
- kalman filters
- intrinsic motivation
- genotype-phenotype map
- empowerment maximization
- applications of stochastic processes
- What is a statistical model by Peter McCullagh in 2002
- Visualizing probabilistic models in Minkowski space with intensive symmetrized Kullback-Leibler embedding by Han Teoh, Katherine Quinn, James Sethna et al in 2020
- Variational inference a review for statisticians by David Blei et al in 2017
- Universal Darwinism as a process of Bayesian inference by John Campbell in 2016
- The Helmholtz Machine by Peter Dayan, Geoffrey Hinton, Radford Neal, and Richard Zemel in 1995
- Statistical rethinking by Richard McElreath in 2020
- Statistical exponential families A digest with flash cards by Frank Nielsen and Vincent Garcia in 2011
- Statistical inference for stochastic simulation models theory and application by Florian Hartig, Andreas Huth, et al in 2011
- Stan
- Simulation of High-Energy Reactions of PArticles
- Regression and Other Stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari in 2020
- Planning as Inference in Epidemiological Dynamics Models by Andrew Warrington, Saeid Naderiparizi, and Frank Wood in 2020
- Pattern recognition and machine learning by Chris Bishop in 2006
- Network inference and biological dynamics by Chris Oates and Sach Mukherjee in 2012
- Multi-Omics Factor Analysis v2 MOFA+ a statistical framework for comprehensive integration of multi-modal single-cell data by Ricard Argelaguet, Oliver Stegle, et al in 2020
- Michael P. H. Stumpf
- Mean field variational inference
- Mechanistic Inference of Brain Network Dynamics with Approximate Bayesian Computation by Timothy West, Vladimir Litvak, et al in 2019
- Jennifer Hill
- Information geometry by Nihat Ay, Jurgen Jost, et al in 2017
- Inferring gene regulatory networks from single-cell data a mechanistic approach by Ulysse Herbach, Olivier Gandrillon, et al in 2017
- Information, physics, and computation by Marc Mezard and Andrea Montanari in 2009
- If a cell was performing probabilistic inference how could this be measured experimentally?
- Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters by James Hensman, Neil Lawrence, and Magnus Rattray in 2013
- George Box
- Gaussian processes for time-series modelling by Roberts, Osborne, Aigrain, et al in 2013
- Frank Wood
- Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics by Mathieu Fourment and Aaron Darling in 2019
- Efficient probabilistic inference in the quest for physics beyond the Standard Model by Atilim Baydin, Frank Wood et al in 2019
- Du Phan
- Du Phan's implementation of Statistical rethinking in pyro
- Cosma Shalizi
- Clustering gene expression time series data using an infinite Gaussian process mixture model by Ian McDowell, Timothy Reddy, Barbara Engelhardt, et al in 2018
- Computational resource demands of a predictive Bayesian brain by Johan Kwisthout and Iris van Rooij in 2020
- Causal inference with Bayes rule by Finnian Lattimore and David Rohde in 2019
- Causal network inference using biochemical kinetics by Chris Oates, Sach Mukherjee et al in 2014
- Build, Compute, Critique, Repeat Data Analysis with latent variable models by David Blei in 2014
- Becoming as inference
- Bayesian non-parametrics and the probabilistic approach to modelling by Zoubin Ghahramani in 2013
- Bayesian reasoning and machine learning by David Barber in 2012
- Automatic Differentiation Variational Inference by Dustin Tran, Andrew Gelman, David Blei et al in 2016
- Approximate Bayesian inference in semi-mechanistic models by Andrej Aderhold, Dirk Husmeier, and Marco Grzegorczyk in 2017
- Andrew Gelman
- An introduction to probabilistic programming by Jan-Willem van de Meent, Frank Wood et al in 2018
- A tutorial on the free-energy framework for modeling perception and learning by Rafal Bogacz in 2017
- A high-bias, low-variance introduction to Machine Learning for physicists by Pankaj Mehta, Charles Fisher, David Schwab, et al in 2018
- A Simulated Annealing Approach to Bayesian Inference by Carlo Albert in 2018