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

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

Business organisations and the legitimation of market inequalities

High levels of economic inequality are associated with a wide range of negative social outcomes. Public opinion surveys, moreover, consistently show that a majority of people are concerned about existing levels of inequality (even as they tend to underestimate how unequal their own societies are.) Why then, have the citizens of many western democracies been so reluctant to support policies and parties that are committed to addressing inequality? This paper, situated in the context of an especially small, distant and open economy, explores the discursive practices of business lobby groups and, more specifically, those groups’ arguments against higher wages at the lower end of the earnings distribution. It argues that these practices simultaneously rely on and reinforce a broad acceptance of the proposition that the social institution of the market is an immutable force that powerfully constrains society’s ability to pursue greater equalit

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

Fiscal Transparency, Gubernatorial Popularity, and the Scale of Government: Evidence from the States.

We explore the effect of transparency of fiscal institutions on the scale of government and gubernatorial popularity using a formal model of accountability. We construct an index of fiscal transparency for the American states from detailed budgetary information. With cross-section data for 1986-1995, we find that - on average and controlling for other influential factors - fiscal transparency increases both the scale of government and gubernatorial popularity. The results, subjected to extensive robustness checks, imply that more transparent budget institutions induce greater effort by politicians, to which voters give higher job approval, on average. Voters also respond by entrusting greater resources to politicians where insittutions are more transparent, leading to larger size of government.

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

Nested sampling for Potts models

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 constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model

Acceleration of energetic particles by large-scale compressible magnetohydrodynamic turbulence

Fast particles diffusing along magnetic field lines in a turbulent plasma can diffuse through and then return to the same eddy many times before the eddy is randomized in the turbulent flow. This leads to an enhancement of particle acceleration by large-scale compressible turbulence relative to previous estimates in which isotropic particle diffusion is assumed.Comment: 13 pages, 3 figures, accepted for publication in Ap

Glitch or anti-glitch: a Bayesian view

The sudden spin-down in the rotation of magnetar 1E 2259+586 observed by Archibald et al. (2013) was a rare event. However this particular event, referred to as an anti-glitch, was followed by another event which Archibald et al. (2013) suggested could either be a conventional glitch or another anti-glitch. Although there is no accompanied radiation activity or pulse profile change, there is decisive evidence for the existence of the second timing event, judging from the timing data. We apply Bayesian Model Selection to quantitatively determine which of these possibilities better explains the observed data. We show that the observed data strongly supports the presence of two successive anti-glitches with a Bayes Factor, often called the odds ratio, greater than 40. Furthermore, we show that the second anti-glitch has an associated frequency change Δν of -8.2 X 10<sup>8</sup> Hz. We discuss the implications of these results for possible physical mechanisms behind this anti-glitch