Model selection and estimation in the matrix normal graphical model

Efficient estimation and model selection in large graphical models

We develop a computationally eecient method to determine the interaction structure in a multidimensional binary sample. We use an interaction model based on orthogonal functions, and give a result on independence properties in this model. Using this result we develop an eecient approximation algorithm for estimating the parameters in a given undi-rected model. To nd the best model, we use a heu...

Strategies for Graphical Model Selection

Spatio-Temporal Graphical Model Selection

We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network. Among other models, the susceptible, infected, recovered (SIR) model for interaction events fall into this framework. We pose the problem as a structure learning problem and ...

Motor Unit Number Estimation in Normal and Parkinsonism Model of Medial Gastrocnemius Muscle in Rats

    Motor units (MUs) reflect the function of the central nervous motor system. Thus, the estimated MU number is a good option to investigate the functional movement disorder in the Parkinson’s disease (PD). The purpose of this study was to compare the estimated MUs number in the medial gastrocnemius (MG) muscle of the normal rats and those with the Parkinsonism. The MG muscle of two age-matche...

Error estimation and model selection

Machine learning algorithms search a space of possible hypotheses and estimate the error of each hypotheses using a sample. Most often, the goal of classification tasks is to find a hypothesis with a low true (or generalization) misclassification probability (or error rate); however, only the sample (or empirical) error rate can actually be measured and minimized. The true error rate of the ret...