title: Continuous probability distributions from finite data.
abstract: Recent approaches to the problem of inferring a continuous probability distribution from a finite set of data have used a scalar field theory for the form of the prior probability distribution. This paper presents a more general form for the prior distribution that has a geometrical interpretation and can improve the specificity of likely solutions. It is also demonstrated that a numerical sampling of the posterior probability distribution can be used as an alternative to a histogram for visualization and to make probabilistic inferences from the data.
authors.affiliation: Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
authors.forename: D M
authors.initials: DM
authors.lastname: Schmidt
mesh_terms: D005583:Fourier Analysis; D016013:Likelihood Functions; D011336:Probability; D012680:Sensitivity and Specificity; D016008:Statistical Distributions