Monte Carlo

Tuesday, May 8, 2018 - 9:00am - 9:50am
Paul Dupuis (Brown University)
A number of methods have been developed for unbiased and efficient approximation of small probabilities and expected values that depend heavily on tail events. Examples include importance sampling and particle splitting methods. However, successfully implementing these methods can require some care. Traditional diagnostics one might use to assess algorithm performance can be misleading, and may suggest the method is working well when in fact it is not. As a consequence, methods that combine design with rigorous performance analysis are particularly useful.
Wednesday, October 20, 2010 - 8:30am - 9:30am
Daniela Calvetti (Case Western Reserve University)

In this talk we propose a probabilistic interpretation of the parameters in the system of differential equations describing a complex cellular brain metabolism model. Uncertainty in the parameter values, variability of the data over a population and errors in the collected data contribute to the variance of the distributions of the parameters. Markov chain Monte Carlo sampling schemes are employed to draw parameter sets which identify models in statistical agreement with the available data and with the a priori belief about the system.

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