In this paper, we study the problem of sampling (exactly) uniformly from the set of linear extensions of an arbitrary partial order. Previous Markov chain techniques have yielded algorithms that generate approximately uniform samples. Here, we create a bounding chain for one such Markov chain, and by using a non-Markovian coupling together with a modified form of coupling from the past, we buil...

An area supervisor managing an area chain of local urban retail stores faces the problem of determining the number of employees and allocating them to retail stores (workforce size and its allocation). We propose a model to maximize the overall expected profit rate of a coordinated area chain where employee absenteeism is uncertain. Retail stores operate as a Markovian loss queueing system. An ...

A classical random walk (St, t ∈ N) is defined by St := t ∑ n=0 Xn, where (Xn) are i.i.d. When the increments (Xn)n∈N are a one-order Markov chain, a short memory is introduced in the dynamics of (St). This so-called “persistent” random walk is nolonger Markovian and, under suitable conditions, the rescaled process converges towards the integrated telegraph noise (ITN) as the time-scale and spa...

Allmost all the available tools for the analysis of Stochastic Petri Nets (SPN) assume that the stochastic nature of the problem is restricted to be a Continuous Time Markov Chain (CTMC), but in reality, there are instances in which the CTMC assumption is too weak. The evolution of the stochastic systems with non-exponential timing becomes a stochastic process, for which in general, no analytic...

Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate techniques. In this paper, we present a technique for approximate inference of conditional probabilities fo...

Allmost all the available tools for the analysis of Stochastic Petri Nets (SPN) assume that the stochastic nature of the problem is restricted to be a Continuous Time Markov Chain (CTMC), but in reality, there are instances in which the CTMC assumption is too weak. The evolution of the stochastic systems with nonexponential timing becomes a stochastic process, for which in general, no analytica...

We investigate the influence of environment noise on control cost and quantum speed limit time (QSLT) in process almost-exact state transmission (AEST) through a spin chain under pulse control. The is immersed its surrounding non-Markovian, finite-temperature heat baths. find that AEST can be realized weak system-bath coupling, low temperature, strong non-Markovian baths effective external Corr...

Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quant...

Consider a Markov chain X assumed to be positive recurrent with limiting probability distribution used to construct a U-statistics. Whereas the asymptotic properties of U -statistics based on independent and identically distributed data are well understood since the sixties the study of this speci…c class of statistics for dependent data has recently received special attention in the statistica...

Among the class of discrete time Markovian processes, two models are widely used, the Markov chain and the Hidden Markov Model. A major di erence between these two models lies in the relation between successive outputs of the observed variable. In a visible Markov chain, these are directly correlated while in hidden models they are not. However, in some situations it is possible to observe both...