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Adaptive Bayesian Nonparametric Smoothing with Markov Random Fields and Shrinkage Priors

Time
Speaker
Jim Faulkner, QERM Ph.D. Candidate

The need to estimate unknown functions or surfaces arises in many disciplines in science and there are many statistical methods available to do this. Our interest lies in using Bayesian nonparametric approaches to estimate unknown functions. One such approach to nonparametric estimation is based on the Gaussian Markov random field priors. This class of computationally efficient and flexible methods is widely used in applications.

Building
Room
207

Probability by Surprise: The Pleasure of Paradoxes

Time
Speaker
Susan Holmes

The main idea of the project, "Probability by Surprise: Teaching with Paradoxes," is to unify the presentation of probability to a heterogenous audience through the interest we have in things that surprise us. Some examples we use in our probability classes include: 'the birthday problem,' 'say red,' 'russian roulette,' 'de Mere's problem,' and 'Monty Hall.' The tools developed are based on discoveries by cognitive pyschologists (in particular Tversky and Kanneman) over the last 20 years, that have not, as yet, been used in teaching probability in this country.

Building
Room
022

Probability by Surprise: The Pleasure of Paradoxes

Time
Speaker
Susan Holmes

The main idea of the project, "Probability by Surprise: Teaching with Paradoxes," is to unify the presentation of probability to a heterogenous audience through the interest we have in things that surprise us. Some examples we use in our probability classes include: 'the birthday problem,' 'say red,' 'russian roulette,' 'de Mere's problem,' and 'Monty Hall.' The tools developed are based on discoveries by cognitive pyschologists (in particular Tversky and Kanneman) over the last 20 years, that have not, as yet, been used in teaching probability in this country.

Building
Room
022