'Stationary distribution' presentation slideshows

Quark Coalescence and Hadron Statistics

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Quark Coalescence and Hadron Statistics

900 views • 80 slides

Random Walks on Graphs: An Overview

Random Walks on Graphs: An Overview. Purnamrita Sarkar. Motivation: Link prediction in social networks. ?. Motivation: Basis for recommendation. Motivation: Personalized search. Why graphs?. The underlying data is naturally a graph Papers linked by citation Authors linked by co-authorship

1.02k views • 71 slides

CSE 473: Artificial Intelligence Spring 2012

CSE 473: Artificial Intelligence Spring 2012. Reasoning about Uncertainty & Hidden Markov Models Daniel Weld. Many slides adapted from Dan Klein, Stuart Russell, Andrew Moore & Luke Zettlemoyer. 1. Stop!. Use lecture 23 version instead. Outline. Probability review

859 views • 70 slides

Genome evolution

Genome evolution. Lecture 6: Inference through sampling. Basic phylogenetics. The paradigm. Phylogenetics. Detecting selection and function. Alignment. Evolutionary rates. Tree. Ancestral Inference on a phylogenetic tree. Learning a model. Rates and transition probabilities.

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607 views • 48 slides

Haplotype Analysis based on Markov Chain Monte Carlo

Haplotype Analysis based on Markov Chain Monte Carlo . By Konstantin Sinyuk. Overview. Haplotype, Haplotype Analysis Markov Chain Monte Carlo (MCMC) The algorithm based on (MCMC) Compare with other algorithms result Discussion on algorithm accuracy. What is haplotype ?.

972 views • 38 slides

LONGITUDINAL INSTABILITIES

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CS 188: Artificial Intelligence Fall 2008

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An Analytical Model for a Two Echelon (S-1, S) Inventory System With Full Lostsales

455 views • 29 slides

Evolutionary Models

Evolutionary Models. CS 498 SS Saurabh Sinha. Models of nucleotide substitution. The DNA that we study in bioinformatics is the end(??)-product of evolution Evolution is a very complicated process Very simplified models of this process can be studied within a probabilistic framework

270 views • 25 slides

Mazes

Mazes. In The Theory of Computer Science. Dana Moshkovitz. C an You Solve This Maze?. T he Solution. Introduction. Objectives : To explore the undirected connectivity problem To introduce randomized computations Overview : Undirected Connectivity Random Walks. Undirected Connectivity.

416 views • 22 slides

The G/M/1 queue

The G/M/1 queue. G. U. Hwang Next Generation Communication Networks Lab. Department of Mathematical Sciences KAIST. Model description. Ref: L. Kleinrock, Queueing systems, vol. I, John Wiley & sons, 1975, chapter 6 (or section 6.4).

410 views • 21 slides

Markov Chain Monte Carlo

Markov Chain Monte Carlo. Gil McVean Tuesday 24 th February 2009. Bayesian inference. In Bayesian statistics we want to learn about the probability distribution of the parameters of interest given the data = the posterior

355 views • 18 slides

A Very Basic Gibbs Sampler for Motif Detection

A Very Basic Gibbs Sampler for Motif Detection . Frances Tong July 28, 2004 Southern California Bioinformatics Summer Institute. What?. What is a motif? What is the biological point? What is the goal? What is a limitation with dynamic programming? What is Gibbs sampling?

511 views • 17 slides

Analysis of Network Diffusion and Distributed Network Algorithms

Analysis of Network Diffusion and Distributed Network Algorithms. Rajmohan Rajaraman Northeastern University, Boston May 2012. Overview of the 4 Sessions. Random walks Percolation processes Branching processes, random graphs, and percolation phenomena Rumors & routes

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Metropolis- Hastings Algorithm

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Stability of the longitudinal coherent modes

Stability of the longitudinal coherent modes. General stability criterion Application to the LHC at top energy. General stability criterion (1/6). Stationary distribution. Synchronous phase shift. Potential well distortion. Perturbation (around the new fixed point). Dispersion relation.

198 views • 8 slides

Entropy Rate of a Markov Chain

Entropy Rate of a Markov Chain. For a stationary Markov chain the entropy rate is given by: Where the conditional entropy is computed using the given stationary distribution. Recall that the stationary distribution ? is the solution of the equations:

528 views • 7 slides