We study the general class of estimators for graphical model structure based on optimizing !1-regularized approximate loglikelihood, where the approximate likelihood uses tractable variational approximations of the partition function. We provide a message-passing algorithm that directly computes the !1 regularized approximate MLE. Further, in the case of certain reweighted entropy approximation...

Efficient implementations of the sum-product algorithm (SPA) for decoding low-density parity-check (LDPC) codes using loglikelihood ratios (LLR) as messages between symbol and parity-check nodes are presented. Various reduced-complexity derivatives of the LLRSPA are proposed. Both serial and parallel implementations are investigated, leading to trellis and tree topologies, respectively. Further...

In this paper, the problem of fast model adaptation for nonnative speakers is addressed from a perspective of model complexity selection. The key challenge lies in reliable complexity selection when only a small amount of adaptation data is available. A novel maximum expected likelihood (MEL) based technique is proposed to enable model complexity selection from using as little as one adaptation...

Maximum likelihood (ML) estimation using Newton’s method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the loglikelihood and its gradient and Hessian. We estimate the gradient and Hessian using Fisher’s identity in combination with a smoothing algorithm. We explore two approximations of the log-likelihood and of the solution of the smoot...

Random utility theory models an agent’s preferences on alternatives by drawing a real-valued score on each alternative (typically independently) from a parameterized distribution, and then ranking the alternatives according to scores. A special case that has received significant attention is the Plackett-Luce model, for which fast inference methods for maximum likelihood estimators are availabl...

The spatial autoregression (SAR) model is a knowledge discovery technique used for mining massive geo-spatial data in many application domains. Estimation of the parameters of the exact SAR model using Maximum Likelihood (ML) theory is computationally very expensive because of the need to compute the logarithm of the determinant (log-det) of a large matrix in the loglikelihood function. In this...

This material is studying MV-algebras and BL-algebras in terms of applicability in Boole algebra. Boole algebras are expanded use in fuzzy logic.