We congratulate the authors for a very interesting article. The key contribution of this paper, as we see it and as suggested in the title, is the introduction of latent parameters to carry out Bayesian analysis with support vector machines. The basic identities (4) and (6) are particularly useful in this regard, which enable one to overcome much of the complexities of a non smooth loss resulti...

Bayes nets (BNs) for relational databases are a major research topic in machine learning and artificial intelligence. When the database exhibits cyclic probabilistic dependencies, measuring the fit of a BN model to relational data with a likelihood function is a challenge [5, 36, 28, 9]. A common approach to difficulties in defining a likelihood function is to employ a pseudo-likelihood; a prom...

We derive a single-pass algorithm for computing the gradient and Fisher information of Vecchia’s Gaussian process loglikelihood approximation, which provides computationally efficient means applying scoring maximizing loglikelihood. The advantages optimization techniques are demonstrated in numerical examples an application to Argo ocean temperature data. new methods find maximum likelihood est...

This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most efficient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood using the EIS strategy (Richard and Zhang, 1998), and detail its implementation for univariate homogene...

This paper addresses the application of optimization transfer to simultaneous statistical estimation of attenuation and activity images in tomographic image reconstruction. Although the technique we propose has wider applicability, we focus on the problem of reconstructing from data acquired via a post-injection transmission scan protocol. In this protocol, emission scan data is supplemented wi...

We present a framework for designing fast and monotonic algorithms for transmission tomography penalizedlikelihood image reconstruction. The new algorithms are based on paraboloidal surrogate functions for the loglikelihood. Due to the form of the log-likelihood function, it is possible to find low curvature surrogate functions that guarantee monotonicity. Unlike previous methods, the proposed ...

We present a framework for designing fast and monotonic algorithms for transmission tomography penalizedlikelihood image reconstruction. The new algorithms are based on paraboloidal surrogate functions for the loglikelihood. Due to the form of the log-likelihood function, it is possible to find low curvature surrogate functions that guarantee monotonicity. Unlike previous methods, the proposed ...

In this paper, we discuss a parsimonious approach to estimation of high-dimensional covariance matrices via the modified Cholesky decomposition with lasso. Two different methods are proposed. They are the equiangular and equi-sparse methods. We use simulation to compare the performance of the proposed methods with others available in the literature, including the sample covariance matrix, the b...

We propose a method for concatenative speech synthesis that permits to obtain a better matching between the logF0 and duration predicted by the prosody module and the waveform generation back-end. The proposed method is based upon our previous multilevel parametric F0 model and Toshiba’s plural unit selection and fusion synthesizer. The method adds a feedback loop from the back-end into the pro...