PGA for M6.5, SS, ZTOR=0 Nonlinearity in NGA1 models does not fully explain the GK Bump Finite Fault Simulations (Stochastic), M6.5, SS, dip=90, Vs30=865 m/s

Source URL: earthquake.usgs.gov

c 2010 INFORMS | isbn[removed]4
doi[removed]educ[removed]INFORMS holds copyright to this article and distributed this copy as a courtesy to the author(s). Additional information, including rights and permissi

Source URL: www.princeton.edu

• Queueing theory
• Markov models
• Mathematical optimization
• Queueing model
• Markov chain
• Optimal control
• Markov decision process
• Dynamic programming
• Poisson process
• Statistics
• Markov processes
• Stochastic processes

Two Meetings on Green Data Storage at DIMACS Organizers: Stark Draper (University of Toronto) Emina Soljanin (Bell Labs – Murray Hill, US) Two short meetings on “Green Data Storage” were held at the Center for Disc

Source URL: dimacs.rutgers.edu

• Stochastic processes
• Mathematics
• Concurrent computing
• Operations research
• Combinatorics
• DIMACS
• Queueing theory
• Queueing model
• Cloud computing
• Statistics
• Formal sciences
• Markov models

Bayesian Nonparametric Learning with semi-Markovian Dynamics by Matthew J Johnson B.S. in Electrical Engineering and Computer Sciences

Source URL: ssg.mit.edu

• Hidden Markov models
• Bayesian statistics
• Markov models
• Hidden semi-Markov model
• Dirichlet process
• Gibbs sampling
• Bayesian inference
• Mixture model
• Non-parametric statistics
• Statistics
• Stochastic processes
• Statistical inference

Artificial Intelligence[removed]–134 Planning and acting in partially observable stochastic domains Leslie Pack Kaelbling a,∗,1,2 , Michael L. Littman b,3 , Anthony R. Cassandra c,1

Source URL: people.csail.mit.edu

• Stochastic control
• Markov models
• Partially observable Markov decision process
• Markov decision process
• Reinforcement learning
• Automated planning and scheduling
• Markov chain
• Kalman filter
• Statistics
• Markov processes
• Dynamic programming

Journal of Artificial Intelligence Research[removed]Submitted 10/05; published[removed]Learning Symbolic Models of Stochastic Domains Hanna M. Pasula

Source URL: people.csail.mit.edu

• Model theory
• Functions and mappings
• Predicate logic
• Formal languages
• First-order logic
• Function
• Interpretation
• FO
• Causality
• Mathematical logic
• Logic
• Mathematics

Approximate Planning in POMDPs with Macro-Actions Georgios Theocharous MIT AI Lab 200 Technology Square Cambridge, MA 02139

Source URL: people.csail.mit.edu

• Stochastic control
• Markov models
• Partially observable Markov decision process
• Models of computation
• Markov decision process
• Automated planning and scheduling
• Reinforcement learning
• Algorithm
• Q-learning
• Statistics
• Markov processes
• Dynamic programming

STOCHASTIC MODELS OF SPACE RADIATION DNA DAMAGE RESPONSES AND CANCER RISKS

Source URL: lowdose.energy.gov

• Radioactivity
• Ionizing radiation
• Relative biological effectiveness
• Radiation therapy
• Radiation
• Health threat from cosmic rays
• Sievert
• Medicine
• Radiobiology
• Health

Package ‘ESGtoolkit’ July 2, 2014 Type Package Title Toolkit for the simulation of financial assets and interest rates models. Version 0.1 Date[removed]

Source URL: cran.r-project.org

• Stochastic processes
• Ggplot2
• Visualization
• R
• Forward price
• Stochastic differential equation
• Yield curve
• Statistics
• Software
• Free software

Telecommunication Systems 21:2–4, 173–204, 2002  2002 Kluwer Academic Publishers. Manufactured in The Netherlands. The Analysis of Queues with Time-Varying Rates for Telecommunication Models WILLIAM A. MASSEY

Source URL: www.princeton.edu

• Markov processes
• Markov models
• Poisson processes
• Queueing model
• Poisson distribution
• M/M/1 queue
• Markov chain
• Offered load
• Point process
• Statistics
• Stochastic processes
• Queueing theory