«normal models»

A Study of the Role of the Maintained-Discharge Parameter in the Divisive Normalization Model of V1 Neurons

The divisive normalization model [Heeger, 1992] accounts successfully for a wide range of phenomena observed in singlecell physiological recordings from neurons in primary visual cortex (V1). Using mathematical analyses and simulation experiments, we investigated the role of the

Bayesian inference for the multivariate skew-normal model: A population Monte Carlo approach

Frequentist and likelihood based methods of inference encounter several difficulties with the multivariate skew-normal model. In spite of the popularity of this class of densities, there are no broadly satisfactory solutions for estimation and testing problems. In this paper we p

A mixed effects multinomial logistic-normal model for forecasting baseball performance

Abstract Prediction of player performance is a key component in the construction baseball team rosters. As result, most prediction models are proprietary property or industrial sports entities, and little known about them. Of those that have been published, main focus has to sepa

Empirical estimates for various correlations in longitudinal-dynamic heteroscedastic hierarchical normal models

In this paper, we first define longitudinal-dynamic heteroscedastic hierarchical normal models. These models can be used to fit longitudinal data in which the dependency structure is constructed through a dynamic model rather than observations. We discuss different methods for es

On the Theory of the Arrhenius-Normal Model with Applications to the Life Distribution of Lithium-Ion Batteries

Typically, in accelerated life testing analysis, only probability distributions possessing shape parameters are used to fit the experimental data, and many with no have been excluded, including fundamental ones like normal distribution, even when they good fitters data. This work

Modeling temporal response characteristics of V1 neurons with a dynamic normalization model

We present a dynamic normalization model to characterize both the transient and the steady-state components of V1 simple and complex cell responses. Primary receptive "eld properties are chie#y determined by the convergence of LGN a!erents. These linear responses are recti"ed, an

Variational Bayes for Regime-Switching Log-Normal Models

The power of projection using divergence functions is a major theme in information geometry. One version of this is the variational Bayes (VB) method. This paper looks at VB in the context of other projection-based methods in information geometry. It also describes how to apply V

There's Waldo! a Normalization Model of Visual Search Predicts Single-trial Human Fixations in an Object Search Task There's Waldo! a Normalization Model of Visual Search

Fast Exact Bayesian Inference for the Hierarchical Normal Model: Solving the Improper Posterior Problem

The hierarchical normal-normal model considered. Standard Empirical Bayes methods underestimate variability because they ignore uncertainty about the hyperparameters. Bayes' theorem solves this problem. We provide fast, exact inference that requires only a simple, univariate nume

Maximum Likelihood Estimation for Matrix Normal Models via Quiver Representations

We study the log-likelihood function and maximum likelihood estimate (MLE) for matrix normal model both real complex models. describe exact number of samples needed to achieve (almost surely) three conditions, namely a bounded function, existence MLEs, uniqueness MLEs. As consequ