نتایج جستجو برای: markov technique
تعداد نتایج: 677864 فیلتر نتایج به سال:
The Finite-State Markov Channel (FSMC) is a discrete time-varying channel whose variation is determined by a finite-state Markov process. These channels have memory due to the Markov channel variation. We obtain the FSMC capacity as a function of the conditional channel state probability. We also show that for i.i.d. channel inputs, this conditional probability converges weakly, and the channel...
In this paper, we study almost sure convergence of a dynamic average consensus algorithm which allows distributed computation of the product of n time-varying conditional probability density functions. These density functions (often called as “belief functions”) correspond to the conditional probability of observations given the state of an underlying Markov chain, which is observed by n differ...
Given the result v0 of a survey and a nested collection of summary statistics that could be used to describe that result, it is natural to ask which of these summary statistics best describe v0. In 1998 Diaconis and Sturmfels presented an approach for determining the conditional significance of a higher order statistic, after sampling a space conditioned on the value of a lower order statistic....
Intrusion detection systems are responsible for diagnosing and detecting any unauthorized use of the system, exploitation or destruction, which is able to prevent cyber-attacks using the network package analysis. one of the major challenges in the use of these tools is lack of educational patterns of attacks on the part of the engine analysis; engine failure that caused the complete training, ...
We present a technique for speeding up inference of structured variables using a prioritydriven search algorithm rather than the more conventional dynamic programing. A priority-driven search algorithm is guaranteed to return the optimal answer if the priority function is an underestimate of the true cost function. We introduce the notion of a probable approximate underestimate, and show that i...
In this paper we develop a simple and accurate analytical technique to determine the loss probability at an access node to an asynchronous transfer mode (ATM) network. This is an important problem from the point of view of admission control and network design. The arrival processes we analyze are the Markov-modulated Poisson process (MMPP) and the Markov-modulated fluid (MMF) process. These arr...
The object of the paper is to see the effect of small stochastic parametric perturbation on a nonlinear interacting system exhibiting Hopf bifurcation. The method is based on the technique of Markov diffusion approximation.
This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.
We present a new estimate of foreground emission in the WMAP data, using a Markov chain Monte Carlo (MCMC) method. The new technique delivers maps of each foreground component for a variety of foreground models with estimates
Hidden Markov models (HMMs) are a common classification technique for time series and sequences in areas such as speech recognition, bio-informatics and handwriting recognition. HMMs are used to model processes which behave according to the Markov property: The next state is only influenced by the current state, not by the past. Although HMMs are popular in handwriting recognition, there are so...
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