For example, if one does not know whether the newborn baby next door is a boy or a girl, the color of decorations on the crib in front of the door may support the hypothesis of one gender or the other; but if behind that door, instead of the crib, a dog kennel is found, the posterior probability that the family next door gave birth to a dog remains small in spite of the "evidence", since one's prior belief in such a hypothesis was already extremely small.The critical point about Bayesian inference, then, is that it provides a principled way of combining new evidence with prior beliefs, through the application of Bayes' rule.Bayesian theory calls for the use of the posterior predictive distribution to do predictive inference, i.e., to predict the distribution of a new, unobserved data point.
But if a hypothesis is extremely unlikely a priori, one should also reject it, even if the evidence does appear to match up.
Only this way is the entire posterior distribution of the parameter(s) used.
By comparison, prediction in frequentist statistics often involves finding an optimum point estimate of the parameter(s)—e.g., by maximum likelihood or maximum a posteriori estimation (MAP)—and then plugging this estimate into the formula for the distribution of a data point.
Developments in fields ranging from neuroscience and psychology to biology and gender studies inform questions asked by our scientists. Explore our research Many researchers also use the Kinsey Institute’s library, collections, and scholarly archives to learn more about the history of sexuality.
Resources include the works of Alfred Kinsey and Masters & Johnson.