'Markov chain' presentation slideshows

1.34k views • 93 slides

Mini-course on algorithmic aspects of stochastic games and related models

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Queueing Theory (Delay Models)

Queueing Theory (Delay Models). Introduction. Total delay of the i-th customer in the system T i = W i + ? i N(t) : the number of customers in the system N q (t) : the number of customers in the queue N s (t) : the number of customers in the service

1.11k views • 62 slides

Application of Met&Roll Weather Generator in Climate Change Impact Studies in Czechia

1.05k views • 61 slides

Application of Met&Roll Weather Generator in Climate Change Impact Studies in Czechia

798 views • 61 slides

Intro. to Stochastic Processes

Intro. to Stochastic Processes. Cheng-Fu Chou. Outline. Stochastic Process Counting Process Poisson Process Markov Process Renewal Process. Stochastic Process. A stochastic process N = {N(t), t ?T} is a collection of r.v., i.e., for each t in the index set T, N(t) is a random variable

1.3k views • 48 slides

Markov chains

Markov chains. Markov Chains. Markov chain model: G=((S,E), ? ) Finite set S of states. Probabilistic transition function ? E ={ ( s,t ) | ? (s)(t) > 0} The graph (S,E) is useful. Markov Chain: Example. 1/3. 1/3. 1/3. Markov Chain: Example. 1/3. 1/3. 1/3. Markov Chain: Example.

790 views • 46 slides

Graph Clustering

Graph Clustering. Why graph clustering is useful?. Distance matrices are graphs ? as useful as any other clustering Identification of communities in social networks Webpage clustering for better data management of web data. Outline. Min s-t cut problem Min cut problem Multiway cut

572 views • 41 slides

Tracking with Local Spatio -Temporal Motion Patterns in Extremely Crowded Scenes

Tracking with Local Spatio -Temporal Motion Patterns in Extremely Crowded Scenes. Present by ???. outline. Introduction Previous work Predicting motion patterns Spatio -temporal transition distribution Discerning pedestrians Experimental results conclusion. introduction.

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Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 2 Sep, 6, 2019

Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 2 Sep, 6, 2019 WAITLISTS INFO https://www.cs.ubc.ca/students/undergrad/courses/waitlists. Lecture Overview. Value of Information and Value of Control. Recap Markov Chain Markov Decision Processes (MDPs)

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Austerity in MCMC Land: Cutting the Computational Budget

502 views • 36 slides

Hidden Markov Models In BioInformatics

Hidden Markov Models In BioInformatics. BY Srikanth Bala. Outline. Markov Chain HMM (Hidden Markov Model) Hidden Markov Models in Bioinformatics Gene Finding Gene Finding Model Viterbi algorithm HMM Advantages HMM Disadvantages Conclusions. Markov Chain.

669 views • 35 slides

Xu Chen Xiaowen Gong Lei Yang Junshan Zhang

A Social Group Utility Maximization Framework with Applications in Database Assisted Spectrum Access. Xu Chen Xiaowen Gong Lei Yang Junshan Zhang School of Electrical, Energy, and Computer Engineering Arizona State University. Outline. Introduction

578 views • 33 slides

Driving with Knowledge from the Physical World

Driving with Knowledge from the Physical World. Jing Yuan, Yu Zheng Microsoft Research Asia. What We Do. Finding the customized and practically fastest driving route for a particular user using (Historical and real-time) Traffic conditions

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Repeats, Pseudogenes etc.

Repeats, Pseudogenes etc. Lecture 11/4. Paper : Wexler et al. Finding Approximate Tandem Repeats in Genomic Sequences Recomb 2004. Goal. Find approximate tandem repeat (ATR) A string of bases repeated consecutively at least twice with small differences between instances. ATR hunter.

491 views • 29 slides

stochastic processes(2)

stochastic processes(2). Dr. Adil Yousif. Lecture 6. Cat and Mouse. Five Boxes [1,2,3,4,5] Cat starts in box 1, mouse starts in box 5 Each turn each animal can move left or right, (randomly) If they occupy the same box, game over (for the mouse anyway). 5 box cat and mouse game. States:.

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G3: Assessing Assistive Technology Usability and Its Effect on the Digital Divide

" Footbridge " by Tim Green, modified and used under CC BY 2.0. G3: Assessing Assistive Technology Usability and Its Effect on the Digital Divide Ryan S. Overdorf. Introduction. My typing vs. my use of Dragon NaturallySpeaking.

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IS3320 Advanced Quantitative Analysis

IS3320 Advanced Quantitative Analysis. Course Objectives. This course aims to: Describe the quantitative analysis approach and it is applications in a real situation. illustrate the use of modeling in quantitative analysis. Use the simplex method to solv e linear programming problems.

2.95k views • 13 slides

Introduction to sampling

Introduction to sampling . Discussion on An Introduction to MCMC for Machine Learning, Andrieu et al., 2001. Sampling. What is sampling? Useful for? Bayesian inference and learning Normalization Marginalization Expectation Optimization Model selection. Sampling.

301 views • 12 slides

PageRank

734 views • 11 slides