On learning strategies for evolutionary Monte Carlo

The real-parameter evolutionary Monte Carlo algorithm (EMC) has been proposed as an effective tool both for sampling from high-dimensional distributions and for stochastic optimization (Liang and Wong, 2001). EMC uses a temperature ladder similar to that in parallel tempering (PT; Geyer, 1991). In contrast with PT, EMC allows for crossover moves between parallel and tempered MCMC chains. In the context of EMC, we introduce four new moves, which enhance its efficiency as measured by the effective sample size. Secondly, we introduce a practical strategy for determining the temperature range and placing the temperatures in the ladder used in EMC and PT. Lastly, we prove the validity of the conditional sampling step of the snooker algorithm, a crossover move in EMC, which extends a result of Roberts and Gilks (1994).     

Prevalence and risk factors of Leptospira infection in urban brown rats (Rattus norvegicus), Vienna,Austria

Urban Ecosystems - Leptospirosis is a worldwide bacterial zoonosis which incidence is expected to increase in conjunction with global change. In urban ecosystems, synanthropic rats are the key...     

PREDICTING RETIREMENT SAVINGS USING SURVEY MEASURES OF EXPONENTIAL‐GROWTH BIAS AND PRESENT BIAS

In a nationally representative sample, we predict retirement savings using survey‐based elicitations of exponential‐growth bias (EGB) and present bias (PB). We find that EGB, the tendency to neglect compounding, and PB, the tendency to value the present over the future, are highly significant and economically meaningful predictors of retirement savings. These relationships hold controlling for cognitive ability, financial literacy, and a rich set of demographic controls. We address measurement error as a potential confound and explore mechanisms through which these biases may operate. Back of the envelope calculations suggest that eliminating EGB and PB would increase retirement savings by approximately 12%. (JEL D91, D14)