acase@tulane.edu / The Beta-binomial distribution is often employed as a model for count data in cases where the observed dispersion is greater than would be expected for the standard binomial distribution. Parameter estimation in this setting is typically performed using a Bayesian approach, which requires specifying appropriate prior distributions for parameters. In the context of many applications, incorporating estimates from previous analyses can offer advantages over naive or diffuse priors. An example of this is in the food security setting, where baseline consumption surveys can inform parameter estimation in crisis situations during which data must be collected hastily on smaller samples of individuals. We have developed an approach for Bayesian updating in the beta-binomial model that incorporates adjustable prior weights and enables inference using a bivariate normal approximation for the mode of the posterior distribution. Our methods, which are implemented in the R programming environment, include tools for the estimation of statistical power to detect changes in parameter values. / 1 / Aleksandra Gorzycka