The keynote speaker for MSSISS 2010 was Dr. Adrian Raftery from the University of Washington.
Probabilistic Projection of HIV/AIDS Prevalence Using Bayesian Melding with Incremental Mixture Importance Sampling (IMIS)
The Joint United Nations Programme on HIV/AIDS (UNAIDS) has started to use Bayesian melding as the basis for its probabilistic projections of HIV prevalence in countries with generalized epidemics. This combines a mechanistic epidemiological model, prevalence data and expert opinion. Initially, the posterior distribution was approximated by sampling-importance-resampling, which is simple to implement, easy to interpret, transparent to users and gave acceptable results for most countries. For some countries, however, this is not computationally efficient because the posterior distribution tends to be concentrated around nonlinear ridges and can also be multimodal. We propose instead Incremental Mixture Importance
Sampling (IMIS), which iteratively builds up a better importance sampling function. This retains the simplicity and transparency of sampling importance resampling, but is much more efficient computationally. It also leads to a simple estimator of the integrated likelihood that is the basis for Bayesian model comparison and model averaging. In simulation experiments and on real data it outperformed both sampling importance resampling and three publicly available generic Markov chain Monte Carlo algorithms for this kind of problem.
Student Organizing Committee
|Joe Sakshuag||Survey Meth.||joesaks[at]umich[dot]edu|
|Michael Elliot||Surv Meth.||mrelliot[at]umich[dot]edu|