How Might a mouse search a Maze for Cheese? heese · State Space Search? As a Constraint Satisfaction Problem? Goal-directed Planning As a rule or production System? What is missing? Ideas in this lecture Objective is to accumulate rewards rather than goal states Task is to generate policies for how to act in all situations rather than a plan for a single starting situation

Consistency e Under the Gauss-Markov assumptionS OLS IS BLUE, but in other cases it wont always be possible to find unbiased estimators o In those cases, we may settle for estimators that are consistent, meaning as n→>∞,the distribution of the estimator collapses to the parameter value Economics 20- Prof anderson

Outline Multiple Sequence AlignmentCarillo-& Lipman, Clustal(W) Position-Specific Scoring Matrices(PSSM) Information content, Shannon entropy Sequence logos Hidden Markov Models Other approaches: Genetic algorithms, expectation maximization, MEME, Gibbs sampler

An interesting property of an M/M/ 1 queue, which greatly simplifies combining these queues into a network, is the surprising fact that the output of an M/M/ queue with arrival rate is a Poisson process of rate This is part of Burke's theorem, which follows from reversibility A Markov chain has the property that P[future present, past] P[future present] Conditional on the present state, future states and past states are independent

Markov Decision Processes and Reinforcement Learning · Motivation earning policies through reinforcement Q values

《应用随机过程教程》教学资源（参考资料）与在算法和智能计算中的应用——第12章 连续时间连续状态的 Markov过程,鞅,Ito积分与随机微分方程（下）

《应用随机过程教程》教学资源（参考资料）与在算法和智能计算中的应用——第16章 离散状态的Markov控制与决策过程简介

《应用随机过程教程》教学资源（参考资料）与在算法和智能计算中的应用——第12章 连续时间连续状态的 Markov过程,鞅,Ito积分与随机微分方程（上）

《应用随机过程教程》教学资源（参考资料）与在算法和智能计算中的应用——第8章 Monte Carlo与Markov Chain Monte Carlo（MCMC）方法

《应用随机过程教程》教学资源（参考资料）与在算法和智能计算中的应用——第10章 隐马氏模型（Hidden Markov Model, HMM）及其应用