computation
concept
- information:: discernible differences
- state:: set of discernible differences
- computation:: description of state changes of a system
- digital computation:: discrete states, deterministic or probabilistic rules of change
TODO Use pulumi to deploy odoo
TODO Complete next Kubernetes tutorial (0/6) @est(1h) @low
TODO Use Pulumi to deploy zero-to-jupyterhub to gke @est(2h) @low
TODO Complete Pulumi crosswalk gke tutorial @est(2h) @low
☐ See process diagram in (2019). SEQprocess: a modularized and customizable pipeline framework for NGS processing in R package.
GATK tutorials:
reveal-md for converting markdown files to reveal.js / revealjs slideshows
Links to this note
- open documents
- Towards A Principled Bayesian Workflow by Michael Betancourt in 2020
- Connect emacs TRAMP to gcp container using gcloud compute config-ssh
- Applied Probabilistic Modeling Principled Bayesian Inference for the Discerning Practitioner by Michael Betancourt in 2020
- digital physics
- approximate bayesian computation
- Dynamic causal modelling of COVID-19 by Karl Friston, Thomas Parr, Jean Daunizeau, Rosalyn Moran, et al in 2020
- An approximation to an efficient fundamental physics curriculum
- the roadmap for web development
- research
- Probabilistic Modeling and Statistical Inference by Michael Betancourt in 2019
- Biology of information lectures at College de France
- free energy principle
- probabilistic inference
- variational inference
- How does monad-bayes compare to pyro?
- How can one setup an inexpensive gke kubernetes cluster with no load balancer using terraform running jupyter hub with instances a containerized jupyter notebook with multiple kernels on Arch Linux?
- genotype-tissue expression GTEx
- Bayesian brain hypothesis
- web development
- variational autoencoder
- turing.jl
- quantum computation
- pyprob
- pyro
- phase transition
- julia
- information geometry
- hacker news
- genotype-phenotype map
- fuzzy file finder
- elasticsearch for pdf libraries
- digital computation
- computational complexity
- cancer evolution
- applications of stochastic processes
- approximate bayesian computation scheme for parameter inference and model selection in dynamical systems by Tina Toni, Michael P. H. Stumpf et al in 2009
- Whatever next? Predictive brains, situated agents, and the future of cognitive science by Andy Clark in 2013
- What Is a Macrostate? Subjective Observations and Objective Dynamics by Cosma Shalizi and Chris Moore in 2003
- The geometry of physics by Theodore Frankel in 2012
- The cellular automaton interpretation of quantum mechanics by Gerard 't Hooft in 2014
- Statistical rethinking by Richard McElreath in 2020
- Statistical inference for stochastic simulation models theory and application by Florian Hartig, Andreas Huth, et al in 2011
- Statistical Physics of Complex Systems by Eric Bertin in 2016
- Some Quantum Mechanical Properties of the Wolfram Model by Jonathan Gorard in 2020
- Quantum field theory for economics and finance by Belal Baaquie in 2018
- Programming Language Foundations in Agda by Philip Wadler in 2018
- Planning as Inference in Epidemiological Dynamics Models by Andrew Warrington, Saeid Naderiparizi, and Frank Wood in 2020
- Notions of intrinsic motivation by Martin Biehl in 2017
- Michael P. H. Stumpf
- 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
- Large deviations and dynamical phase transitions in stochastic chemical networks by Lazarescu and Esposito in 2019
- Introduction to single-cell Variational Inference
- Intelligence and spirit by Reza Negarestani in 2018
- Information, physics, and computation by Marc Mezard and Andrea Montanari in 2009
- Husserlian phenomenology
- How is digital physics related to the free energy principle?
- GpABC a Julia package for approximate bayesian computation with Gaussian process emulation by Evgeny Tankhilevich, Michael P. H. Stumpf et al in 2020
- From computation to consciousness by Joscha Bach in 2014
- From computation to life The challenge of a science of organization by Walter Fontana in 2020
- Expanding the Active Inference Landscape More Intrinsic Motivations in the Perception-Action Loop by Martin Biehl, Daniel Polani, et al in 2018
- Efficient exact inference for dynamical systems with noisy measurements using sequential approximate bayesian computation by Yannik Schalte and Jan Hasenauer in 2020
- Efficient probabilistic inference in the quest for physics beyond the Standard Model by Atilim Baydin, Frank Wood et al in 2019
- Current best practices in single‐cell RNA‐seq analysis a tutorial by Malta Luecken and Fabian Theis in 2019
- Computational resource demands of a predictive Bayesian brain by Johan Kwisthout and Iris van Rooij in 2020
- Becoming as inference
- An introduction to manifolds by Loring Tu in 2010
- 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 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
- A Class of Models with the Potential to Represent Fundamental Physics by Stephen Wolfram in 2020