The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate ...

Finite mixtures of normal distributions are attractive in identifying the underlying group structure in the data. However, it is a challenging task to do statistical inference in normal mixture models using the method of maximum likelihood, due to the unbounded likelihood and the existence of multiple roots to the likelihood equation including a so-called spurious root. In this article we propo...

We present Bayesian analyses of matrix-variate normal data with conditional independencies induced by graphical model structuring of the characterizing covariance matrix parameters. This framework of matrix normal graphical models includes prior specifications, posterior computation using Markov chain Monte Carlo methods, evaluation of graphical model uncertainty and model structure search. Ext...

The field of cognitive neuropsychology centers around two coupled goals: to use patterns of cognitive deficits in brain-damaged patients to inform theories and models of how cognitive processes are carried out by the brain, and to apply existing models to explain the specific deficits of individual patients in order to design more effective strategies for remediating these deficits. The roots o...

Multivariate analysis of fMRI data has benefited substantially from advances in machine learning. Most recently, a range of probabilistic latent variable models applied to fMRI data have been successful in a variety of tasks, including identifying similarity patterns in neural data (Representational Similarity Analysis and its empirical Bayes variant, RSA and BRSA; Intersubject Functional Conne...

Valmária Rocha da Silva ∗ Fernando Antônio da Silva Moura † Abstract The main aim of this work is to propose two important connected extensions of the Fay and Heriot (1979) area level small area estimation model that might be of practical and theoretical interests. The first extension allows for the sampling error to be non-symmetrically distributed. This is important for the case that the samp...

Kripke-style models with two accessibility relations, one intuitionistio and the other modal, are given for analogues of the modal system K based on Heyting's propositional logic. I t is shown that these two relations can combine with each other in various ways. Soundness and completeness are proved for systems with only the necessity operator, or only the possibility operator, or both. Embeddi...

When estimating logistic-normal models, the integral appearing in the marginal likelihood is analytically intractable, so that numerical methods such as GaussHermite quadrature (GH) are needed. When the dimensionality increases, the number of quadrature points becomes too high. A possible solution can be found among the Quasi-Monte Carlo (QMC) methods, because these techniques yield quite good ...

This paper, a sequel to "Models for normal intuitionistie modal logics" by M. Bo~i6 and the author, which dealt with intuitionistic analogues of the modal system K, deals similarly with intuitionistic analogues of systems stronger than K, and, in particular, analogues of $4 and $5. For these propositional logics Kripke-style models with two accessibility relations, one intuitionistic and the ot...