One conjecture in both deep learning and classical connectionist viewpoint is that the biological brain implements certain kinds of deep networks as its backend. However, to our knowledge, a detailed correspondence has not yet been set up, which is important if we want to bridge between neuroscience and machine learning. Recent researches emphasized the biological plausibility of LinearNonlinea...

We propose a nonlocal Poisson-Fermi model for ionic solvent that includes ion size effects and polarization correlations among water molecules in the calculation of electrostatic potential. It includes the previous Poisson-Fermi models as special cases, and its solution is the convolution of a solution of the corresponding nonlocal Poisson dielectric model with a Yukawa-type kernel function. Mo...

inventory control is one of the most important issues in supply chain management. in this paper, a three-echelon production, distribution, inventory system composed of one producer, a set of wholesalers and retailers is considered. costumers' demands can be approximated by a normal distribution and the inventory policy is a kind of continuous review (r, q). in this paper, a model based on ...

We consider the Poisson sigma model associated to a Poisson manifold. The perturbative quantization of this model yields the Kontsevich star product formula. We study here the classical model in the Hamiltonian formalism. The phase space is the space of leaves of a Hamiltonian foliation and has a natural groupoid structure. If it is a manifold then it is a symplectic groupoid for the given Pois...

Poisson regression models for count variables have been utilized in many applications. However, in many problems overdispersion and zeroinflation occur. We study in this paper regression models based on the generalized Poisson distribution (Consul (1989)). These regression models which have been used for about 15 years do not belong to the class of generalized linear models considered by McCull...

Categorical data frequently arise in applications in the social sciences. In such applications,the class of log-linear models, based on either a Poisson or (product) multinomial response distribution, is a flexible model class for inference and prediction. In this paper we consider the Bayesian analysis of both Poisson and multinomial log-linear models. It is often convenient to model multinomi...

In this paper, we introduce a new Poisson mixture model for count panel data where the underlying Poisson process intensity is determined endogenously by consumer latent utility maximization over a set of choice alternatives. This formulation accommodates the choice and count in a single random utility framework with desirable theoretical properties. Individual heterogeneity is introduced throu...

Starting with the basic Poisson statistical model of a counting measurement process, 'extraPoisson' variance or 'overdispersion' are included by assuming that the Poisson parameter representing the mean number of counts itself comes from another distribution. The Poisson parameter is assumed to be given by the quantity of interest in the inference process multiplied by a lognormally distributed...

The theory of formation of an ideal image has been described which shows that the gray level in an image [ollows the Poisson distribution. Based on this concept, various algorithms for object background classification have been developed. Proposed algorithms involve either the maximum entropy principle or the minimum X statistic. The appropriateness of the Poisson distributiou is fun:her streng...