The entropy rate quantifies the amount of uncertainty or disorder produced by any dynamical system. In a spiking neuron, this uncertainty translates into the amount of information potentially encoded and thus the subject of intense theoretical and experimental investigation. Estimating this quantity in observed, experimental data is difficult and requires a judicious selection of probabilistic ...

This paper considers how to estimate Bayesian credible and highest probability density (HPD) intervals for parameters of interest and provides a simple Monte Carlo approach to approximate these Bayesian intervals when a sample of the relevant parameters can be generated from their respective marginal posterior distribution using a Markov chain Monte Carlo (MCMC) sampling algorithm. We also deve...

Herein, we propose the Bayesian approach for constructing confidence intervals both coefficient of variation a log-normal distribution and difference between coefficients two distributions. For first case, was compared with large-sample, Chi-squared, approximate fiducial approaches via Monte Carlo simulation. second method variance estimates recovery (MOVER), modified MOVER, using The results s...

‎Lindley distribution has received considerable attention in the statistical literature due to its simplicity‎. ‎In this paper‎, ‎we consider the problem of predicting the failure times of experimental units that are censored in a right-censored sample‎‎ when the underlying lifetime is Lindley distributed‎. ‎The maximum likelihood predictor‎, ‎the Bes...

This paper studies large sample properties of a Bayesian approach to inference about slope parameters $\gamma$ in linear regression models with structural break. In contrast the conventional that does not take into account uncertainty unknown break location $\tau$, we consider incorporates such uncertainty. Our main theoretical contribution is Bernstein-von Mises type theorem (Bayesian asymptot...

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...

BACKGROUND Prediction intervals can be calculated for predicting cancer incidence on the basis of a statistical model. These intervals include the uncertainty of the parameter estimates and variations in future rates but do not include the uncertainty of assumptions, such as continuation of current trends. In this study we evaluated whether prediction intervals are useful in practice. METHODS...