Title: | Markov Processes: Markov Chains, Poisson Processes, Brownian Motion |

Speaker: | Nadiya Fink Visiting Assistant Professor Mathematics and Computer Science Albion College Albion,, MI |

Abstract: | The Markov property indicates that, with knowledge of the current state, previous trajectories are irrelevant for predicting the probability of the future of a process. A Markov chain is a discrete-time stochastic (i.e. random) process possessing the Markov property. Probabilities and expected values on a Markov chain can be evaluated by a technique called First Step Analysis. An analogous technique can be applied to continuous-time processes. We will discuss an elementary introduction to Markov chains and First Step Analysis, followed by a broader description and discussion of the long-term behavior of Markov chains. Further, we will get acquainted with the Poisson Processes which are continuous-time processes with finite number of states, and, finally, will overview the continuous processes and their applications. |

Location: | Palenske 227 |

Date: | 3/27/2008 |

Time: | 3:10 PM |

@abstract{MCS:Colloquium:NadiyaFink:2008:3:27, author = "{Nadiya Fink}", title = "{Markov Processes: Markov Chains, Poisson Processes, Brownian Motion}", address = "{Albion College Mathematics and Computer Science Colloquium}", month = "{27 March}", year = "{2008}" }