# Difference between revisions of "Probability Seminar"

(→Tuesday, February 14, Jean-Luc Thiffeault, UW-Madison) |
(→Thursday, January 31, Bret Larget, UW-Madison) |
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== Thursday, January 31, Bret Larget, UW-Madison == | == Thursday, January 31, Bret Larget, UW-Madison == | ||

− | Title: | + | Title: Approximate conditional independence of separated subtrees and phylogenetic inference |

− | |||

+ | |||

+ | Abstract: | ||

+ | Bayesian methods to reconstruct evolutionary trees from aligned DNA | ||

+ | sequence data from different species depend on Markov chain Monte | ||

+ | Carlo sampling of phylogenetic trees from a posterior distribution. | ||

+ | The probabilities of tree topologies are typically estimated with the | ||

+ | simple relative frequencies of the trees in the sample. When the | ||

+ | posterior distribution is spread thinly over a very large number of | ||

+ | trees, the simple relative frequencies from finite samples are often | ||

+ | inaccurate estimates of the posterior probabilities for many trees. We | ||

+ | present a new method for estimating the posterior distribution on the | ||

+ | space of trees from samples based on the approximation of conditional | ||

+ | independence between subtrees given their separation by an edge in the | ||

+ | tree. This approximation procedure effectively spreads the estimated | ||

+ | posterior distribution from the sampled trees to the larger set of | ||

+ | trees that contain clades (sets of species in subtrees) that have been | ||

+ | sampled, even if the full tree is not part of the sample. The | ||

+ | approximation is shown to be accurate for many data sets and is | ||

+ | theoretically justified. We also explore a consequence of this result | ||

+ | that may lead to substantial increases in computational efficiency for | ||

+ | sampling trees from posterior distributions. Finally, we present an | ||

+ | open problem to compare rates of convergence between the simple | ||

+ | relative frequency approach and the approximation approach. | ||

==Tuesday, February 14, Jean-Luc Thiffeault, UW-Madison== | ==Tuesday, February 14, Jean-Luc Thiffeault, UW-Madison== |

## Revision as of 17:33, 28 January 2013

## Spring 2013

Thursdays in 901 Van Vleck Hall at 2:25 PM, unless otherwise noted. If you would like to receive announcements about upcoming seminars, please visit this page to sign up for the email list.

## Thursday, January 31, Bret Larget, UW-Madison

Title: Approximate conditional independence of separated subtrees and phylogenetic inference

Abstract: Bayesian methods to reconstruct evolutionary trees from aligned DNA sequence data from different species depend on Markov chain Monte Carlo sampling of phylogenetic trees from a posterior distribution. The probabilities of tree topologies are typically estimated with the simple relative frequencies of the trees in the sample. When the posterior distribution is spread thinly over a very large number of trees, the simple relative frequencies from finite samples are often inaccurate estimates of the posterior probabilities for many trees. We present a new method for estimating the posterior distribution on the space of trees from samples based on the approximation of conditional independence between subtrees given their separation by an edge in the tree. This approximation procedure effectively spreads the estimated posterior distribution from the sampled trees to the larger set of trees that contain clades (sets of species in subtrees) that have been sampled, even if the full tree is not part of the sample. The approximation is shown to be accurate for many data sets and is theoretically justified. We also explore a consequence of this result that may lead to substantial increases in computational efficiency for sampling trees from posterior distributions. Finally, we present an open problem to compare rates of convergence between the simple relative frequency approach and the approximation approach.

## Tuesday, February 14, Jean-Luc Thiffeault, UW-Madison

Title: Biomixing and large deviations

Abstract: As fish, micro-organisms, or other bodies move through a fluid, they stir their surroundings. This can be beneficial to some fish, since the plankton they eat depends on a well-stirred medium to feed on nutrients. Bacterial colonies also stir their environment, and this is even more crucial for them since at small scales there is no turbulence to help mixing. I will discuss a simple model of the stirring action of moving bodies through a fluid. An attempt will be made to explain existing data on the displacements of small particles, which exhibits probability densities with exponential tails. A large-deviation approach helps to explain some of the data, but mysteries remain.

## Tuesday, March 5, Janosch Ortmann, University of Toronto

Title: TBA

Abstract: TBA

## Thursday, March 14, Brian Rider, Temple University

Title: TBA

Abstract: TBA

## Thursday, March 21, Neil O'Connell, University of Warwick

Title: TBA

Abstract: TBA

## Thursday, April 11, Kevin Lin University of Arizona

Title: TBA

Abstract: TBA

## Thursday, April 25, Fraydoun Rezakhanlou, UC - Berkeley

Title: TBA

Abstract: TBA

## Wednesday, May 1, Bálint Vető, University of Bonn

Title: TBA

Abstract: TBA