1 Discrete-time Markov Chains Let {Xk } be a discrete-time stochastic process which takes on values in a countable set S, called the state space. {Xk } is called a Discrete-time Markov chain (or simply a Markov chain, w

Source URL: www.ifp.illinois.edu

Chao-Yuan Wu | | http://chaoyuan.org RESEARCH INTERESTS Statistical machine learning, neural netwoks, and recommender systems.

Source URL: www.andrew.cmu.edu

• Stochastic processes
• Recommender system
• Dirichlet process
• Machine learning

The Dynamics of Distributions in Continuous-Time Stochastic Models (a) Christian Bayer(a) and Klaus Wälde(b) WeierstraßInstitute Berlin and (b) Johannes-Gutenberg University Mainz1

Source URL: www.waelde.com

• Stochastic processes
• Markov chain
• FokkerPlanck equation
• Markov process
• Markov kernel
• Probability distribution
• Brownian motion
• It diffusion
• Bellman equation

Cognitive Science–43 Copyright © 2015 Cognitive Science Society, Inc. All rights reserved. ISSN: printonline DOI: cogsFaster Teaching via POMDP Planning

Source URL: cocosci.berkeley.edu

• Dynamic programming
• Markov processes
• Stochastic control
• Partially observable Markov decision process
• Educational psychology
• Educational technology
• Automated planning and scheduling
• Markov decision process
• Concept learning
• Intelligent tutoring system

The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty Ron Alterovitz Thierry Sim´eon

Source URL: goldberg.berkeley.edu

• Probability theory
• Mathematical analysis
• Mathematics
• Markov processes
• Robot control
• Classical mechanics
• Markov models
• Dynamic programming
• Motion planning
• Stochastic roadmap simulation
• Probabilistic roadmap
• Markov decision process

Learning Conditional Latent Structures from Multiple Data Sources Viet Huynh1(B) , Dinh Phung1 , Long Nguyen2 , Svetha Venkatesh1 , and Hung H. Bui3 1

Source URL: dept.stat.lsa.umich.edu

• Statistics
• Probability
• Statistical natural language processing
• Probability distributions
• Stochastic processes
• Machine learning
• Statistical models
• Latent Dirichlet allocation
• Dirichlet process
• Dirichlet distribution
• Mixture model
• Gibbs sampling

An Alternative Prior Process for Nonparametric Bayesian Clustering Hanna M. Wallach Department of Computer Science University of Massachusetts Amherst

Source URL: www-stat.wharton.upenn.edu

• Statistics
• Stochastic processes
• Statistical theory
• Probability
• Hierarchical Dirichlet process
• Dirichlet process
• PitmanYor process
• Chinese restaurant process
• Dirichlet distribution
• Mixture model
• Concentration parameter
• Pattern recognition

CURRICULUM VITAE for S.G.Walker EDUCATION Sep 1976-NovSutton Manor High School, Sutton, Surrey. Oct 1984-JunOriel College, Oxford University (awarded Open Exhibition on entry to Oriel College). Oct 1992-Sep

Source URL: www.ma.utexas.edu

• Statistical inference
• Statistical theory
• Stochastic processes
• Statistical models
• Bayesian inference
• Statistical forecasting
• Bayesian statistics
• Dirichlet process
• Bayesian
• Statistics
• Mixture model

Stochastic dynamics for adaptation and evolution of microorganisms Sylvie Méléard (Ecole polytechnique) Understanding the adaptation and evolution of populations is a huge challenge, in particular for microorganisms si

Source URL: www.7ecm.de

• Biology
• Evolutionary biology
• Genetics
• Molecular evolution
• Evolutionary dynamics
• Evolution
• Evolutionary invasion analysis
• Natural selection
• Mutation
• Adaptation
• Stochastic process
• Population genetics

mTunes: Efficient Post-Silicon Tuning of Mixed-Signal/RF Integrated Circuits Based on Markov Decision Process Manzil Zaheer and Fa Wang Chenjie Gu

Source URL: manzil.ml

• Markov models
• Markov processes
• Probability theory
• Applied mathematics
• Statistics
• Receiver
• Dynamic programming
• Markov decision process
• Stochastic control
• Tuner
• Artificial neural network
• Tuning