Exploring Multi-Modal Distributions with Nested Sampling

In performing a Bayesian analysis, two difficult problems often emerge. First, in estimating the parameters of some model for the data, the resulting posterior distribution may be multi-modal or exhibit pronounced (curving) degeneracies. Secondly, in selecting between a set of competing models, calculation of the Bayesian evidence for each model is computationally expensive using existing methods such as thermodynamic integration. Nested Sampling is a Monte Carlo method targeted at the efficient calculation of the evidence, but also produces posterior inferences as a by-product and therefore provides means to carry out parameter estimation as well as model selection. The main challenge in implementing Nested Sampling is to sample from a constrained probability distribution. One possible solution to this problem is provided by the Galilean Monte Carlo (GMC) algorithm. We show results of applying Nested Sampling with GMC to some problems which have proven very difficult for standard Markov Chain Monte Carlo (MCMC) and down-hill methods, due to the presence of large number of local minima and/or pronounced (curving) degeneracies between the parameters. We also discuss the use of Nested Sampling with GMC in Bayesian object detection problems, which are inherently multi-modal and require the evaluation of Bayesian evidence for distinguishing between true and spurious detections.Comment: Refereed conference proceeding, presented at 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineerin

Entropic criterion for model selection

Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why uses this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha, we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.Comment: 10 pages. Accepted for publication in Physica A, 200

Relativistic particle acceleration in developing Alfv\'{e}n turbulence

A new particle acceleration process in a developing Alfv\'{e}n turbulence in the course of successive parametric instabilities of a relativistic pair plasma is investigated by utilyzing one-dimensional electromagnetic full particle code. Coherent wave-particle interactions result in efficient particle acceleration leading to a power-law like energy distribution function. In the simulation high energy particles having large relativistic masses are preferentially accelerated as the turbulence spectrum evolves in time. Main acceleration mechanism is simultaneous relativistic resonance between a particle and two different waves. An analytical expression of maximum attainable energy in such wave-particle interactions is derived.Comment: 15 pages, 9 figures, 1 tabl

Analytic Continuation of Quantum Monte Carlo Data by Stochastic Analytical Inference

We present an algorithm for the analytic continuation of imaginary-time quantum Monte Carlo data which is strictly based on principles of Bayesian statistical inference. Within this framework we are able to obtain an explicit expression for the calculation of a weighted average over possible energy spectra, which can be evaluated by standard Monte Carlo simulations, yielding as by-product also the distribution function as function of the regularization parameter. Our algorithm thus avoids the usual ad-hoc assumptions introduced in similar algortihms to fix the regularization parameter. We apply the algorithm to imaginary-time quantum Monte Carlo data and compare the resulting energy spectra with those from a standard maximum entropy calculation

Sovereign Net Worth: An Analytical Framework

The Fiscal Responsibility Act requires the Crown to articulate targets for a series of fiscal variables, including net worth. Given the dramatic improvement in the fiscal position in recent years, a critical policy question relates to how (and which) measures of Crown net worth should be targeted. This paper sets out a framework for targeting Crown net worth. It does so by supplementing the GAAP-based measure with forward-looking information about spending and tax revenue. The paper argues that targeting net worth for the Crown requires the estimation of a path, rather than a static level.

Educational Outcomes and Obstacles for Children and Youth in Foster Care in Hawai‘i.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018