Predicting the future as Bayesian inference: people combine prior knowledge with observations when estimating duration and extent.

Journal of Experimental Psychology: General Predicting the Future as Bayesian Inference: People Combine Prior Knowledge With Observations When Estimating Duration and Extent

Predicting the future 1 Running head: PREDICTING THE FUTURE Predicting the future as Bayesian inference: People combine prior knowledge with observations when estimating duration and extent

Predicting the future is a basic problem that people have to solve every day and a component of planning, decision-making, memory, and causal reasoning. This paper presents five experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should combine prior knowledge with observed data. Comparing this...

Teacakes, Trains, Taxicabs and Toxins: a Bayesian Account of Predicting the Future the Copernican Anthropic Principle

This paper explores how people make predictions about the future. Statistical approaches to predicting the future are discussed, focussing on the method for predicting the future suggested by J. R. Gott (1993). A generalized Bayesian form of Gott's method is presented, and a speciic psychological model suggested. Three experiments show that the predictions people make about the future are consi...

بهبود یادگیری رفتار روبات سیار دارای خطا در سنسورهای آن با استفاده از شبکه بیزین

In this paper a new structure based on Bayesian networks is presented to improve mobile robot behavior, in which there exist faulty robot sensors. If a robot likes to follow certain behavior in the environment to reach its goal, it must be capable of making inference and mapping based on prior knowledge and also should be capable of understanding its reactions on the environment over time. Old ...

Inference on Pr(X > Y ) Based on Record Values From the Power Hazard Rate Distribution

In this article, we consider the problem of estimating the stress-strength reliability $Pr (X > Y)$ based on upper record values when $X$ and $Y$ are two independent but not identically distributed random variables from the power hazard rate distribution with common scale parameter $k$. When the parameter $k$ is known, the maximum likelihood estimator (MLE), the approximate Bayes estimator and ...