Bayesian Methods Developments in Microsimulation

• Activity Number: 408
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Laura Hatfield*+
• Companies: Harvard Medical School
• Keywords: statistical computing ; Bayesian inference ; model checking ; prediction

Abstract

Microsimulation is a method of modeling a population of units that may interact with other units, transition among states according to probabilistic rules, form and dissolve groups, and undergo birth and death processes. The development of microsimulation has been greatest in the fields of economics, traffic planning, epidemiology, and demography, while research into statistical properties of these models has lagged. Strengths of microsimulation include the ability to predict the impact of novel interventions when existing data offer little information, incorporate nonlinearities in the responses of units to transition rules, and program possibly complex dependencies among units. Major challenges include the need to specify a large number of input parameters and transition rules and (usually) the lack of a likelihood function. In this talk, I will review the contributions that Bayesian thinking can make to microsimulations, including parameter calibration and estimation, variance quantification, decision making, model checking, and sensitivity analysis. I will also compare and contrast microsimulation with similar methods that are more familiar to practicing Bayesian statisticians.