We develop a nonparametric Bayesian method that explores the infinite space of latent features and finds the best subset in the sense of posterior probability. When the data appear in several groups, there should be different measures reflecting the differences between the groups. We formalize this as a nested Indian buffet process (nIBP) by assuming different measures according to the specific...

The Shannon entropy, and related quantities such as mutual information, can be used to quantify uncertainty and relevance. However, in practice, it can be difficult to compute these quantities for arbitrary probability distributions, particularly if the probability mass functions or densities cannot be evaluated. This paper introduces a computational approach, based on Nested Sampling, to evalu...

Nested sampling is a new Monte Carlo method by Skilling [1] intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a con...

We comment on several aspects of Skilling's paper presented at Valencia 8. In particular we prove the convergence in probability of the algorithm for a wide class of situations, comment on its potential utility and discuss aspects where further work is needed to assess the approach.

In this paper, rate distortion performance of nested sampling and coprime sampling is studied. It is shown that with the increasing of distortion, the data rate decreases. With these two sparse sampling algorithms, the data rate is proved to be much less than that without sparse sampling. With the increasing of sampling spacings, the data rate decreases at certain distortion, which is because w...

Title of Thesis: Performance and Robustness Analysis of Co-Prime and Nested Sampling Ali Koochakzadeh, Master of Science, 2016 Thesis Directed By: Professor Piya Pal Department of Electrical and Computer Engineering Coprime and nested sampling are well known deterministic sampling techniques that operate at rates significantly lower than the Nyquist rate, and yet allow perfect reconstruction of...

Listings Sparse and Coprime Sampling: Benefits, Challenges and Future Directions Piya Pal University of Maryland, College Park 2305 A V Williams University of Maryland College Park MD 20742 [email protected] Coprime and nested sampling have been recently introduced as powerful sparse deterministic sampling techniques that can exploit statistical properties of signals to sample them at rates sign...

We present Ymer, a tool for verifying probabilistic transient properties of stochastic discrete event systems. Ymer implements both statistical and numerical model checking techniques. We focus on two features of Ymer: distributed acceptance sampling and statistical model checking of nested probabilistic statements.