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Links to this note
- statistical physics
- open documents
- Towards A Principled Bayesian Workflow by Michael Betancourt in 2020
- Connect emacs TRAMP to gcp container using gcloud compute config-ssh
- computation
- The information geometry of 2-field functional integrals by Eric Smith in 2019
- scientific collaboration
- Easy and hard in the origin of life by Eric Smith in 2019
- Biology of information lectures at College de France
- Physics from Symmetry by Jakob Schwichtenberg in 2015
- free energy principle
- system-environment distinction
- variational inference
- genotype-tissue expression GTEx
- renormalization
- pyro
- phase transition
- partial information decomposition
- mutual information
- maximal mutual information principle
- information geometry
- genotype-phenotype map
- evolution in fluctuating environments
- collectors fallacy
- biology
- bioinformatics
- applications of stochastic processes
- Whatever next? Predictive brains, situated agents, and the future of cognitive science by Andy Clark in 2013
- 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 by Chieh Wu in 2015
- Universal Darwinism as a process of Bayesian inference by John Campbell in 2016
- Thermodynamics of Open Chemical Reaction Networks Theory and Applications by Massimiliano Esposito in 2020
- Theoretical investigations of an information geometric approach to complexity by Sean Ali and Carlo Cafaro in 2017
- The demon in the machine by Paul Davies in 2019
- The Value of Information for Populations in Varying Environments by Rivoire and Leibler in 2011
- Statistical exponential families A digest with flash cards by Frank Nielsen and Vincent Garcia in 2011
- Simulation of High-Energy Reactions of PArticles
- Self-organization in nonequilibrium systems from dissipative structures to order through fluctuations by Nicolis and Prigogine in 1977
- Relating Fisher Information to order parameters by Mikhail Prokopenko, Rosalind Wang, et al in 2011
- Regression and Other Stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari in 2020
- Quantum Processes A Whiteheadian Interpretation of Quantum Field Theory by Frank Hattich
- PyClone Statistical inference of clonal population structure in cancer by Roth and Shah in 2014
- Pattern recognition and machine learning by Chris Bishop in 2006
- Optimal Renormalization Group Transformation from Information Theory by Lenggenhager, Ringel, Huber, and Koch-Janusz in 2018
- Notions of intrinsic motivation by Martin Biehl in 2017
- Nonnegative Decomposition of Multivariate Information by Paul Williams and Randall Beer in 2010
- Naftali Tishby
- Michael P. H. Stumpf
- Measuring Clonal Evolution in Cancer with Genomics by Williams, Sottoriva, and Graham in 2019
- Markov blankets, information geometry and stochastic thermodynamics by Parr and Friston in 2020
- Karl Friston
- Introductory Lectures on Stochastic Population Systems by Donald Dawson in 2017
- Information geometry by Nihat Ay, Jurgen Jost, et al in 2017
- Information Theory for Fields by Torsten Enslin in 2019
- Intelligence and spirit by Reza Negarestani in 2018
- Intrinsic and extrinsic thermodynamics for stochastic population processes with multi-level large-deviation structure by Eric Smith in 2020
- Information Geometry and Population Genetics by Julian Hofrichter, Jurgen Jost, and Tat Dat Tran in 2017
- Information, physics, and computation by Marc Mezard and Andrea Montanari in 2009
- How to win in a winner-takes-all world by Neil Irwin in 2019
- How is digital physics related to the free energy principle?
- Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures by Thalia Chan, Michael P. H. Stumpf, and Ann C. Babtie in 2017
- Expanding the Active Inference Landscape More Intrinsic Motivations in the Perception-Action Loop by Martin Biehl, Daniel Polani, et al in 2018
- Epistemology An introduction to the theory of knowledge by Nicholas Rescher in 2003
- Effective fluctuation and response theory by Polettini and Esposito in 2018
- Doi-Peliti Path Integral Methods
- Becoming as inference
- A tutorial on the free-energy framework for modeling perception and learning by Rafal Bogacz in 2017
- A free energy principle for a particular physics by Karl Friston in 2019
- A high-bias, low-variance introduction to Machine Learning for physicists by Pankaj Mehta, Charles Fisher, David Schwab, et al in 2018