Conditional spectrum record selection faithful to causative earthquake parameter distributions

Faithful Representation Of Separable Input Distributions

A geometric approach to data representation incorporating informationtheoretic ideas is presented. The task of finding a faithful representation, where the input distribution is evenly partitioned into regions of equal mass, is addressed. For input consisting of mixtures of statistically independent sources, we treat independent component analysis (ICA) as a computational geometry problem. Firs...

Estimates of Parameter Distributions for Optimal Action Selection

MCMC methods to approximate conditional predictive distributions

Sampling from conditional distributions is a problem often encountered in statistics when inferences are based on conditional distributions which are not of closed-form. Several Markov chain Monte Carlo (MCMC) algorithms to simulate from them are proposed. Potential problems are pointed out and some suitable modifications are suggested.Approximations based on conditioning sets are also explored...

Improper Regular Conditional Distributions

At the bottom of page 1614, we are not precise in the definition of a Borel space. The condition should have read that there is a one-to-one measurable function with measurable inverse between (Ω,B) and (E,E), where E is a Borel subset of the reals and E is the Borel σ-field of subsets of E. After the remaining corrections below, our use of the term “Borel space” conforms with this definition. ...

#p-complete Conditional Distributions

We study conditional probability from the perspective of complexity theory of functions and operators in analysis, building on work by Ko (1983), Friedman (1984), and Kawamura and Cook (2010). For some random variable X in {0, 1}N whose distribution is continuous and polynomial-time computable, and some polynomial-time computable function f : {0, 1}N → [0, 1] for which the random variable f(X) ...