Geometric nested sampling: sampling from distributions defined on non-trivial geometries

Sampling Distributions Sampling Distributions

On Sampling from Multivariate Distributions

Let X1, X2, . . . , Xn be a set of random variables. Suppose that in addition to the prior distributions of these random variables we are also given linear constraints relating them. We ask for necessary and sufficient conditions under which we can efficiently sample the constrained distributions, find constrained marginal distributions for each of the random variables, etc. We give a tight cha...

Sampling and Sampling Distributions

A sample is a group of objects or readings taken from a population for counting or measurement. We shall distinguish between two kinds of populations — finite populations and infinite populations. The sample must be random, meaning that all possible samples of a particular size must have equal probabilities of being chosen from the population. This will prevent bias in the sampling process. Mos...

Sampling from Gibbs distributions

Sampling from Gibbs distributions by Eric Joseph Vigoda Doctor of Philosophy in Computer Science University of California at Berkeley Professor Alistair Sinclair, Chair This thesis considers computational questions concerning statistical mechanical models of idealized physical systems. The equilibrium state of the physical system is described by a probability distribution over the allowed con g...

Sampling from Discrete Distributions:

At the 2001 FCSM Research Conference, Greene et al. introduced a problem in editing and imputation based on fire data. The editing problem consists of imputing values to cells in a 2x2 contingency table subject to extensive item and unit nonresponse. Mathematically, the nonresponse creates an incomplete 2-way table with partial counts for individual cells and marginal totals. Statistically, the...