'Markov process' diaporamas de présentation

Upper bounds on k -SAT

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Return to Risk Limited website: RiskLimited

Return to Risk Limited website: www.RiskLimited.com. Valuation and Hedging of Power-Sensitive Contingent Claims for Power with Spikes: a Non-Markovian Approach. Valery A. Kholodnyi February 25, 2004 Houston, Texas. Introduction.

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Hao Wang University of Oregon

A Class of Conditional Independent . Branching Particle Systems and. Their Interacting Limit Superprocesses. Hao Wang University of Oregon. Outline. A new model for interacting branching particle systems.

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Quickest Detection and Its Allications

Zhu Han Department of Electrical and Computer Engineering University of Houston, Houston, TX, USA. Quickest Detection and Its Allications. Outline. Introduction Basics Markov stopping time Quickest Detection Sequential detection Bayesian detection CUSUM test Applications

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On Systems with Limited Communication PhD Thesis Defense

On Systems with Limited Communication PhD Thesis Defense. Jian Zou May 6, 2004. Motivation I. Information theoretical issues are traditionally decoupled from consideration of decision and control problems by ignoring communication constraints.

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Markov Chains

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Channel Allocation for GPRS

Channel Allocation for GPRS. From: IEEE Tran. Veh. Technol., Vol. 50, no. 2, 2001. Author: P. Lin and Y.-B. Lin CSIE, NTU & CSIE, NCTU. Outline. Introduction GPRS architecture Packet data logical channel Channel allocation schemes System model Analysis results

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Queuing Theory

Queuing Theory. Queuing Theory. Queuing Theory deals with systems of the following type: Typically we are interested in how much queuing occurs or in the delays at the servers. Server Process( es ). Input Process. Output. Queuing Theory Notation.

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Cryptography and Network Security Chapter 18

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Markov Chain Monte Carlo

Markov Chain Monte Carlo. Prof. David Page transcribed by Matthew G. Lee. Markov Chain. A Markov chain includes A set of states A set of associated transition probabilities

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Cryptography and Network Security Chapter 18

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A particle representation for the heat equation solution

A particle representation for the heat equation solution . Krzysztof Burdzy University of Washington. Fleming-Viot model. N = population size (constant in time ) = ecological niche carrying capacity. Free space Population distribution converges to a fractal structure.

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Markov Chains as a Learning Tool

Markov Chains as a Learning Tool. 0.6. 0.4. 0.8. rain. no rain. 0.2. Markov Process Simple Example. Weather: raining today 40% rain tomorrow 60% no rain tomorrow not raining today 20% rain tomorrow 80% no rain tomorrow. Stochastic Finite State Machine:.

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Lecture 7

Lecture 7. Poisson Processes (a reminder) Some simple facts about Poisson processes The Birth/Death Processes in General Differential-Difference Equations The Pure Birth Process (Generating our own Poisson process from scratch) How are queues related to Markov chains The M/M/1 queue

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Markov Models and Simulations

Markov Models and Simulations. Yu Meng Department of Computer Science and Engineering Southern Methodist University. Outline. Markov model/process/chain/property/HMM Matlab simulations. Markov Process/chain/model/etc. Markov Process Markov Model Markov Chain Markov Property

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Alignment III PAM Matrices

Alignment III PAM Matrices. PAM250 scoring matrix. 0. + 3. + ( -3 ). + 1. Scoring Matrices. S = [s ij ] gives score of aligning character i with character j for every pair i, j. S T P P C T C A. = 1. Scoring with a matrix.

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Collaborative Particle Filters for Group Tracking

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Introduction of Markov Chain Monte Carlo

Introduction of Markov Chain Monte Carlo. Jeongkyun Lee. Contents. Usage Why MCMC is called MCMC MCMC methods Appendix Reference. Usage. Goal : 1) Estimate an unknown target distribution (or posterior) for a complex function, or 2) draw samples from the distribution. Simulation

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Polymerase Chain Reaction: A Markov Process Approach

Polymerase Chain Reaction: A Markov Process Approach. Mikhail V. Velikanov et al. J. theor. Biol. 1999 Summarized by ??? 2003.8.12. Contents. Introduction Markov Process Model for Primer Extension Model for Multi-Cycle PCR Runs Numerical Results Discussion. Introduction.

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Monte Carlo Simulation

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