Maximum a Posteriori Estimation by Search in Probabilistic Programs

We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variables. BaMC is an anytime MAP search algorithm applicable to any combination of random variables and dependencies. We compare BaMC to other MAP estimation algorithms and show that BaMC is faster and more robust on a range of probabilistic models.Comment: To appear in proceedings of SOCS1

Risk, uncertainty and pasture investment decisions

The private decisions of farmers to invest in new technologies interest economists because these decisions influence the rate of farm productivity growth and the returns to public investment in agricultural research and development. Economic analysis of decisions to invest in new technologies on farms involves considering the effects of these decisions on the profitability and risk of the farm business. This is done routinely using whole-farm economic models and techniques such as stochastic simulation. Such analysis can be used to predict the extent to which a technology is likely to be adopted in equilibrium, when the consequences of adoption are known to all potential adopters. Until this equilibrium is reached, however, potential adopters of new technologies face uncertainty about the consequences of adoption. This alters expectations about the effects on profitability and risk of adoption, and hence alters investment decisions. The resolution of uncertainty over time through learning is therefore a key determinant of the rate at which new technologies are adopted, and hence should be represented in dynamic economic models which seek to explain these decisions.Farm Management,