Multiple electrodes are now a standard tool in neuroscience research that make it possible
to study the simultaneous activity of several neurons in a given brain region or across …

We provide a short overview of importance sampling—a popular sampling tool used for
Monte Carlo computing. We discuss its mathematical foundation and properties that …

Analysis of data from neurophysiological investigations can be challenging. Particularly
when experiments involve dynamics of neuronal response, scientific inference can become …

In a 1935 paper and in his book Theory of Probability, Jeffreys developed a methodology for
quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now …

Subjectivism has become the dominant philosophical foundation for Bayesian inference. Yet
in practice, most Bayesian analyses are performed with so-called “noninformative” priors …

To compute a Bayes factor for testing H 0: ψ= ψ0 in the presence of a nuisance parameter β,
priors under the null and alternative hypotheses must be chosen. As in Bayesian estimation …

Differential geometry provides an aesthetically appealing and oftenrevealing view of
statistical inference. Beginning with anelementary treatment of one-parameter statistical …

Abstract Markov chain Monte Carlo (MCMC) methods make possible the use of flexible
Bayesian models that would otherwise be computationally infeasible. In recent years, a …

Measuring agreement between a statistical model and a spike train data series, that is,
evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to …

We consider two-stage models of the kind used in parametric empirical Bayes (PEB)
methodology, calling them conditionally independent hierarchical models. We suppose that …