Integration Of Stochastic Models Research Articles

A simulation-optimization approach for scheduling in stochastic freight transportation

Abstract In this paper, a new Simulation-Based Optimization Model (SBO-Model) is proposed to solve scheduling problem in stochastic multimodal freight transportation systems. The model is applied to find optimal services schedule in a real-world case study. In order to handle demand and travel time inherent variability, the stochastic service network design problem is addressed. Simulation modeling is used to efficiently account for real stochastic behavior with skewed continuous distributions. Such distinctive distribution shapes were commonly reported in transportation research studies that addressed the travel time reliability modeling. Results indicate that the SBO-Model can indeed provide reliable service schedules even under realistic complex stochasticity. The main finding is that, in order to solve efficiently such stochastic optimization problem, we need to go beyond the mean and variance estimates by considering the empirical distributions of uncertain parameters. Specifically, when the data exhibit skewness and/or multimodality, which are commonly found due to the traffic congestion. The originality of this work lies in the integration of stochastic models, commonly used in the transportation research field, for solving logistics planning problem generally addressed by Operations Research community.

Formula omitted]-Gaussian mixture modelling for speaker recognition

Gaussian mixture model is the conventional approach employed in speaker recognition tasks. Although it is efficient to model specific speaking characteristics of a speaker, especially in quiet environments, its performance in noisy conditions is still far from the human cognitive process. Recently, a new method of α -integration of stochastic models has been proposed based on psychophysical experiments that suggests α -integration is used in a human brain. In this paper, we proposed a method to extend the conventional GMM to the α -integrated GMM ( α -GMM) to model personal speaking traits. Model parameters were re-estimated recursively based on a given data set. The experiments showed that the new approach significantly outperforms the traditional method, especially on telephony speech.

Integration of Stochastic Models by Minimizing α-Divergence

When there are a number of stochastic models in the form of probability distributions, one needs to integrate them. Mixtures of distributions are frequently used, but exponential mixtures also provide a good means of integration. This letter proposes a one-parameter family of integration, called alpha-integration, which includes all of these well-known integrations. These are generalizations of various averages of numbers such as arithmetic, geometric, and harmonic averages. There are psychophysical experiments that suggest that alpha-integrations are used in the brain. The alpha-divergence between two distributions is defined, which is a natural generalization of Kullback-Leibler divergence and Hellinger distance, and it is proved that alpha-integration is optimal in the sense of minimizing alpha-divergence. The theory is applied to generalize the mixture of experts and the product of experts to the alpha-mixture of experts. The alpha-predictive distribution is also stated in the Bayesian framework.