In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic function of its continuous parents, the joint density function does not exist. Conditional linear Gaussian distributions can handle such cases when the deterministic function is linear and the continuous variables have a multi-variate normal distribution. In this paper, operations requ...

In this paper a realistic cellular automata model is proposed to simulate traffic flow at single-lane roundabouts. The proposed model is built on fine grid Cellular Automata (CA), so it is able to simulate actual traffic flow more realistically. Several important novel features are employed in our model. Firstly, 1.5-second rule is used for the headway (=distance /speed) in carfollowing process...

The availability of intraday stock/index return in the web facilitates the improvement of return volatility estimation over the traditional method that is based on inter-day return data. Truncated Levy process distribution is used to extract the intraday return distribution parameters. The calibration to the volatility for Black-Scholes option pricing is studied using the data from Levy-Gaussia...

A recursive filter for effective suppression of impulse noise is presented in this paper. The proposed work estimates the noise corruption level and the position of impulses in the first stage. The appropriate filter parameters for a detail-preserving restoration of the corrupted pixels are determined based on these estimations. The filtering process assumes a Gaussian spatial profile in the ne...

When a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the joint density function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle such cases when the continuous variables have a multi-variate normal distribution and the discrete variables do not have continuous parents. In this paper, operations require...

Formally exact series expressions are derived for the entropy ~information content! of a time series or signal by making systematic expansions for the higher-order correlation functions using generalized Kirkwood and Markov superpositions. Termination of the series after two or three terms provides tractable and accurate approximations for calculating the entropy. Signals generated by a Gaussia...

The perceived position of a stationary Gaussian window of a Gabor target shifts in the direction of motion of the Gabor's carrier stimulus, implying the presence of interactions between the specialized visual areas that encode form, position, and motion. The purpose of this study was to examine the temporal and spatial properties of this illusory motion-induced position shift (MIPS). We measure...

We consider multistage stochastic linear optimization problems combining joint dynamic probabilistic constraints with hard constraints. We develop a method for projecting decision rules onto hard constraints of wait-and-see type. We establish the relation between the original (infinite-dimensional) problem and approximating problems working with projections from different subclasses of decision...

In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic function of its continuous parents, the joint density function for the variables in the network does not exist. Conditional linear Gaussian distributions can handle such cases when the deterministic function is linear and the continuous variables have a multi-variate normal distributio...

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...