PRISM is a probabilistic logic programming formalism which allows learning parameters from examples through its graphical EM algorithm. PRISM is aimed at modelling generative processes in the compact first-order logic representation. It facilitates model selection by providing three scoring functions Bayesian Information Criterion (BIC), Cheeseman-Stutz (CS) and Variational free energy. This pa...

In this paper, we address the theoretical limitations in reconstructing sparse signals (in a known complete basis) using compressed sensing framework. We also divide the CS to non-blind and blind cases. Then, we compute the Bayesian Cramer-Rao bound for estimating the sparse coefficients while the measurement matrix elements are independent zero mean random variables. Simulation results show a ...

Decision making in light of uncertain and incomplete knowledge is one of the central themes in statistics and machine learning. Probabilistic Bayesian models provide a mathematically rigorous framework to formalise the data acquisition process while making explicit all relevant prior knowledge and assumptions. The resulting posterior distribution represents the state of knowledge of the model a...

Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes compu...

This paper investigates the problem of sparse support detection (SSD) via a detection-oriented algorithm named Bayesian hypothesis test via belief propagation (BHT-BP) [7],[8]. Our main focus is to compare BHT-BP to an estimation-based algorithm, called CS-BP [3], and show its superiority in the SSD problem. For this investigation, we perform a phase transition (PT) analysis over the plain of t...

Long-standing challenges in cluster expansion (CE) construction include choosing how to truncate the expansion and which crystal structures to use for training. Compressive sensing (CS), which is emerging as a powerful tool for model construction in physics, provides a mathematically rigorous framework for addressing these challenges. A recently-developed Bayesian implementation of CS (BCS) pro...

Identification of neurocognitive predictors of substance dependence is an important step in developing approaches to prevent addiction. Given evidence of inhibitory control deficits in substance abusers (Monterosso et al., 2005; Fu et al., 2008; Lawrence et al., 2009; Tabibnia et al., 2011), we examined neural processing characteristics in human occasional stimulant users (OSU), a population at...

Human behaviour analysis has important applications in many emergency management problems as Intelligent Video Surveillance (IVS) for crowding situations. In many VS systems, supervision from a human operator is needed; for example, in overcrowding situations, the experience of a security operator is crucial in order to redirect people flow for the maintenance of an acceptable safety level. An ...

The widely accepted intuition that the important properties of solids are determined by a few key variables underpins many methods in physics. Though this reductionist paradigm is applicable in many physical problems, its utility can be limited because the intuition for identifying the key variables often does not exist or is difficult to develop. Machine learning algorithms (genetic programmin...

The theory of compressive sensing (CS) asserts that an unknown signal x ∈ CN can be accurately recovered from m measurements with m N provided that x is sparse. Most of the recovery algorithms need the sparsity s = ‖x‖0 as an input. However, generally s is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesia...