papers
Links to this note
- open documents
- approximate bayesian computation
- Dynamic causal modelling of COVID-19 by Karl Friston, Thomas Parr, Jean Daunizeau, Rosalyn Moran, et al in 2020
- reading list
- The information geometry of 2-field functional integrals by Eric Smith in 2019
- Population Extinction on a Random Fitness Seascape by Bertrand Ottino-Loffler and Mehran Kardar in 2020
- Growth dynamics in naturally progressing chronic lymphocytic leukaemia by Michaela Gruber, Gad Getz, and Catherine Wu in 2020
- Bayesian informal logic and fallacy by Kevin Korb in 2003
- A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data by Dimitrios Vavoulis et al in 2020
- probabilistic inference
- variational inference
- genotype-tissue expression GTEx
- library
- invariant measures of random dynamical systems as analogs to attractors in deterministic dynamical systems
- cancer evolution
- asexual evolution
- approximate bayesian computation scheme for parameter inference and model selection in dynamical systems by Tina Toni, Michael P. H. Stumpf et al in 2009
- Wolfram physics project
- What is a statistical model by Peter McCullagh in 2002
- The Self-Evidencing Brain by Jakob Hohwy in 2016
- Subclonal reconstruction of tumors by using machine learning and population genetics by Giulio Caravagna, Marc Williams, Trevor Graham, and Andrea Sottoriva in 2020
- Statistical exponential families A digest with flash cards by Frank Nielsen and Vincent Garcia in 2011
- Splatter simulation of single-cell RNA sequencing data by Luke Zappia, Belinda Phipson, and Alicia Oshlack
- Relating Fisher Information to order parameters by Mikhail Prokopenko, Rosalind Wang, et al in 2011
- Relations and dependencies between morphological characters Jürgen Jost in 2017
- Renormalization for Philosophers by Butterfield and Bouatta in 2014
- Quantification of subclonal selection in cancer from bulk sequencing data by Williams and Graham in 2018
- Predicting Evolutionary Constraints by Identifying Conflicting Demands in Regulatory Networks by Manjunatha Kogenaru, Frank Poelwijk, Sander Tans et al in 2020
- Optimal transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming by Geoffrey Schiebinger, Aviv Regev, Eric Lander, et al in 2019
- Normal Form for Renormalization Groups by Archishman Raju, James Sethna, et al in 2019
- Nonnegative Decomposition of Multivariate Information by Paul Williams and Randall Beer in 2010
- Network inference and biological dynamics by Chris Oates and Sach Mukherjee in 2012
- Multiscale Structure in Eco-Evolutionary Dynamics by Blake Stacey in 2015
- Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases by Zheng Hu, Christina Curtis, et al in 2020
- 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
- Mechanistic Inference of Brain Network Dynamics with Approximate Bayesian Computation by Timothy West, Vladimir Litvak, et al in 2019
- Master equations and the theory of stochastic path integrals by Markus Weber and Erwin Frey in 2017
- Mathematical modeling with single-cell sequencing data by Heyrim Cho and Russell Rockne in 2019
- Large deviations and dynamical phase transitions in stochastic chemical networks by Lazarescu and Esposito in 2019
- Jurgen Jost
- Intrinsic and extrinsic thermodynamics for stochastic population processes with multi-level large-deviation structure by Eric Smith in 2020
- Inferring what to do (and what not to) by Thomas Parr in 2020
- Inferring Interaction Networks From Multi-Omics Data by Johann Hawe, Fabian Theis, and Matthias Heinig in 2019
- 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
- Gene regulatory network inference from sparsely sampled noisy data by Atte Aalto, Jorge Goncalves, et al in 2020
- Fibration symmetries uncover the building blocks of biological networks by Flaviano Morone and Hernan Makse in 2020
- Expanding the Active Inference Landscape More Intrinsic Motivations in the Perception-Action Loop by Martin Biehl, Daniel Polani, et al in 2018
- Evolutionary game theory theoretical concepts and applications to microbial communities by Erwin Frey in 2010
- Efficient exact inference for dynamical systems with noisy measurements using sequential approximate bayesian computation by Yannik Schalte and Jan Hasenauer in 2020
- Effective Field Theories, Reductionism and Scientific Explanation by Stephan Hartmann in 2001
- Defining Network Topologies that Can Achieve Biochemical Adaptation by Wenzhe Ma, Hana El-Samad, Wendell Lim, and Chao Tang in 2009
- Doi-Peliti Path Integral Methods
- Current best practices in single‐cell RNA‐seq analysis a tutorial by Malta Luecken and Fabian Theis in 2019
- 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
- Between-region genetic divergence reflects the mode and tempo of tumor evolution by Ruping Sun, Zheng Hu, Andrea Sottoriva, Trevor Graham, and Christina Curtis in 2017
- Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing by Romain Lopez, Michael Jordan, Nir Yosef et al in 2018
- Approximate Bayesian inference in semi-mechanistic models by Andrej Aderhold, Dirk Husmeier, and Marco Grzegorczyk in 2017
- A theory of incremental compression by Arthur Franz et al in 2019
- A tutorial on the free-energy framework for modeling perception and learning by Rafal Bogacz in 2017
- A framework for parameter estimation and model selection from experimental data in systems biology using approximate bayesian computation by Juliane Liepe, Michael P. H. Stumpf et al in 2014
- A Simulated Annealing Approach to Bayesian Inference by Carlo Albert in 2018