Modelling based on probabilistic inference can be used to estimate the quality of information delivered by a military sensor network. Different modelling tools have complementary characteristics that can be leveraged to create an accurate model open to intuitive and efficient querying. In particular, stochastic process models can be used to abstract away from the physical reality by describing ...

In today’s competitive business and industrial environment, it is becoming more crucial than ever to assess precisely process losses due to non-compliance to customer specifications. To assess these losses, industry is extensively using Process Capability Indices (PCIs) for performance evaluation of their processes. Determination of the performance capability of a stable process using the stand...

The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbersome to construct. In this work we introduce a novel formulation of DGPs b...

We examine the dynamic formation and stochastic evolution of networks connecting individuals. The payoff to an individual from an economic or social activity depends on the network of connections among individuals. Over time individuals form and sever links connecting themselves to other individuals based on the improvement that the resulting network offers them relative to the current network....

In this paper we consider deterministic and stochastic shortest path problems with an infinite, possibly uncountable, number of states. The objective is to reach or approach a special destination state through a minimum cost path. We use an optimal control problem formulation, under assumptions that parallel those for finite-node shortest path problems, i.e., there exists a path to the destinat...

the present study sustainable management of guilan saravan sylvan park was planned. the used method in the form of decision support model in three10-year period was studied for parks of various states and with considering the economic, ecological and social criteria during the years 2008 to 2038. by using hierarchical fuzzy analysis method, the reaching to an optimal level of economic, ecologic...

Speech is time variable stochastic process. But the HMM model is stationary and time-independent. We present a new model as Hidden Dynamic HMM model to describe the dynamic property of speech. A hidden layer of dynamic property is interpolated between the observation and state. Estimated Dynamic Property and Predicted Dynamic Property is introduced to describe the hidden dynamic property. The r...

Graph transformation systems (GTS) have been proposed for high-level stochastic modelling of dynamic systems and networks. The resulting systems can be described as semi-Markov processes with graphs as states and transformations as transitions. The operational semantics of such processes can be explored through stochastic simulation. In this paper, we develop the basic theory of stochastic grap...

Dynamic lot sizing problem is one of the significant problem in industrial units and it has been considered by  many researchers. Considering the quantity discount in  purchasing cost is one of the important and practical assumptions in the field of inventory control models and it has been less focused in terms of stochastic version of dynamic lot sizing problem. In  this paper, stochastic dyn...

This paper addresses the application of neural network research to a theory of autonomous systems. Neural networks , while enjoying considerable success in autonomous systems applications, have failed to provide a rm theoretical underpinning to neural systems embedded in their natural ecological context. This paper proposes a stochastic formulation of such an embedding. A neural system derived ...