FlipboardIcon version of the Flipboard logoThermodynamics Fluctuation Theorem Uncertainty Relationaps.org - Yoshihiko Hasegawa and Tan Van Vu Author(s): Yoshihiko Hasegawa and Tan Van VuThe fluctuation theorem is the fundamental equality in nonequilibrium thermodynamics that is used to …

FlipboardIcon version of the Flipboard logoMonte Carlo Flow-based generative models for Markov chain Monte Carlo in lattice field theoryaps.org - M. S. Albergo, G. Kanwar, and P. E. Shanahan Author(s): M. S. Albergo, G. Kanwar, and P. E. ShanahanA Markov chain update scheme using a machine-learned flow-based generative model is proposed …

Towards Data Scienceadded this to Reinforcement LearningReinforcement Learning Reinforcement Learning (DDPG and TD3) for News Recommendationtowardsdatascience.com - Mike Watts` Reinforcement learning as-is is a pretty hard topic. When I started to dig deeper, I realized the need for a good explanation. This article, coupled …

Dewayne Cowlesadded this to AIAlgorithms Introduction to Markov Chains in F# | SkillsCastskillsmatter.com A Markov Chain is a stochastic model describing a sequence of possible events, in which the probability of each event depends only on the state …

MMichael Thompsonadded this to Bayesian Guest ratings outshine brand in hotel selectiontraveldailymedia.com - Simon Willmore Understanding how travellers make decisions about where to stay is, unsurprisingly, very important to hoteliers and property managers. The factors …

Canada Markov models—Markov chainsnature.com - Jasleen K. Grewal, Martin Krzywinski, Naomi Altman You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re …

aaozora himeadded this to Self StudyProbability Introduction to Reinforcement Learning : Markov-Decision Processtowardsdatascience.com - Ayush Singh In a typical Reinforcement Learning (RL) problem, there is a learner and a decision maker called agent and the surrounding with which it interacts is …

Albert Tavares de Almeida (Lord Albior)added this to TechPopulation α- Rank: Multi-Agent Evaluation by Evolutionnature.com - Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Perolat, Remi Munos In this section, we concisely outline the game-theoretic concepts and methods necessary to understand the remainder of the paper. We also introduce a …

Towards Data Scienceadded this to Editors' PicksProbability Markov Chain Models in Sportstowardsdatascience.com - Sean Carver A model describes mathematically what we expect from data — in this case, from sports data. One simple type of model, called a Markov chain, finds …

Towards Data Scienceadded this to Editors' PicksStatistics Bayesian inference problem, MCMC and variational inferencetowardsdatascience.com - Joseph Rocca Bayesian inference is a major problem in statistics that is also encountered in many machine learning methods. For example, Gaussian mixture models, …

Highsnobietyadded this to All StoriesReddit Reddit’s CEO Shares His Favorite Subredditshighsnobiety.com - Isabelle Hore-Thorburn Reddit CEO Steve Huffman spends a lot of time on “the front page of the internet” and at this year’s Cannes Lions advertising conference he revealed …

Young Postadded this to NewsChinese University of Hong Kong How to win at Monopoly by using the power of mathsscmp.com - By Wong Tsui-kai CommentsTo post comments please log in register or Login with facebookSubheadline: It’s not always about buying the most expensive properties, says a …

Towards Data Scienceadded this to Machine LearningThe Great Gatsby Summarizing The Great Gatsby using Natural Language Processingtowardsdatascience.com - Andrew Oliver For those unfamiliar with graph theory, it’s very simple. Elementary graphs have two parts: nodes and edges. A node represents a real-world concept …

Towards Data Scienceadded this to Machine LearningProbability Introduction to Hidden Markov Modelstowardsdatascience.com - Tomer Amit Let us first give a brief introduction to Markov Chains, a type of a random process. We begin with a few “states” for the chain, {S₁,…,Sₖ}; For …

Harshadded this to Analytics & Data SciencePredictive Analytics IoT and Predictive Analytics: What We're Driving Towardinformationweek.com - Pierre DeBois Founder, Zimana Learn how research of predictive analytics can impact models used in the transportation sector.

Ricardo Garza Garzaadded this to Machine Learning & AutomationFlipboardIcon version of the Flipboard logoReinforcement Learning Generation of ice states through deep reinforcement learningaps.org - Kai-Wen Zhao, Wen-Han Kao, Kai-Hsin Wu, and Ying-Jer Kao Author(s): Kai-Wen Zhao, Wen-Han Kao, Kai-Hsin Wu, and Ying-Jer KaoWe present a deep reinforcement learning framework where a machine agent is …

Towards Data Scienceadded this to Editors' PicksProbability MCMC Intuition for Everyonetowardsdatascience.com - Rahul Agarwal MCMC is made up of two terms Monte Carlo and Markov Chains. Let us talk about the individual terms one by one. In simple terms, we can think of Monte …

SSean MacRaeadded this to Data Science in BusinessFlipboardIcon version of the Flipboard logo Gentle Approach to Linear Algebra, with Machine Learning Applicationsdatasciencecentral.com - Vincent Granville This simple introduction to matrix theory offers a refreshing perspective on the subject. Using a basic concept that leads to a simple formula for …

MMarc Hansenadded this to Learning AnalyticsFlipboardIcon version of the Flipboard logoLinear Algebra Computer Science Theory for the Information Age, Spring 2012.cmu.edu In the first 50 odd years of its existence, computer science and the mathematical theory supporting it have flourished, enabling the widespread use …

MMike Stirlingadded this to Curiously Interesting Electronics Gadget Book: The AI creativity codeelectronicsweekly.com - Alun Williams Okay, it's not a gadget book as such, but I think it may be of interest to Gadget Master readers - a look at how AI is being used to learn to write, …

Towards Data Scienceadded this to Machine LearningProbability Markov Chains and HMMstowardsdatascience.com - Maël Fabien Let’s start by defining what a stochastic model is. It is essentially a discrete time process indexed at times 1,2,… that takes values, called …

IKITOadded this to Science & TechFlipboardIcon version of the Flipboard logoMH370 Mathematical analysis suggests new area for missing MH370 searchcosmosmagazine.com Modelling of buoy behaviour in ocean currents pushes likely crash site further north. Andrew Masterson reports.

Towards Data Scienceadded this to Machine LearningProbability When to ‘Buy the Dip’towardsdatascience.com - Osho Jha A Gentle Introduction to Hidden Markov Models for Volatility Regime Detection Motivation: “Buy the dip” — it’s a frustratingly simple piece of advice. …

FlipboardIcon version of the Flipboard logoMonte Carlo Unbiased Markov chain Monte Carlo methods | Department of Statisticsstanford.edu - statistics.stanford.edu Monte Carlo estimators, based on Markov chains or interacting particle systems, are typically biased when run with a finite number of iterations (or …

MMichael Thompsonadded this to BayesianStatistics Bayes vs. the Invaders! Part Two: Abnormal Distributionsweirddatascience.net This post continues our series on developing statistical models to explore the arcane relationship between UFO sightings and population. The previous …

Seeking AlphaInvesting National Instruments Pre-Earnings TradeSeeking Alpha - Damon Verial We are playing a stock with rather illiquid options this time, but the probability and payoff curves look too good to ignore. The stock in question …

MMichael Zauzigadded this to Machine Learning and The FutureData Science (Markov chain) Monte Carlo doesn’t “explore the posterior”columbia.edu - Bob Carpenter [Edit: (1) There’s nothing dependent on Markov chain—the argument applies to any Monte Carlo method in high dimensions. (2) No, (MC)MC is not not …

nnomadoeadded this to Data Science From ScratchProbability Introduction to Markov chainstowardsdatascience.com - Joseph Rocca In 1998, Lawrence Page, Sergey Brin, Rajeev Motwani and Terry Winograd published “The PageRank Citation Ranking: Bringing Order to the Web”, an …

Probability Probability - The Science of Uncertainty and Dataedx.org - www.edx.org The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic …

FlipboardIcon version of the Flipboard logoProbability The PageRank computationstanford.edu - nlp.stanford.edu Continuing for several steps, we see that the distribution converges to the steady state of . In this simple example, we may directly calculate this …