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Clustering large and complex data sets whose partitions may adopt arbitrary shapes remains a difficult challenge. Part of this challenge comes from the difficulty in defining a similarity measure between the data points that captures the underlying geometry of those data points. In this paper, we propose an algorithm, DCG++ that generates such a similarity measure that is data-driven and ultrametric. DCG++ uses Markov Chain Random Walks to capture the intrinsic geometry of data, scans possible scales, and combines all this information using a simple procedure that is shown to generate an ultrametric. We validate the effectiveness of this similarity measure within the context of clustering on synthetic data with complex geometry, on a real-world data set containing segmented audio records of frog calls described by mel-frequency cepstral coefficients, as well as on an image segmentation problem. The experimental results show a significant improvement on performance with the DCG-based ultrametric compared to using an empirical distance measure. [ABSTRACT FROM AUTHOR]

The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. [ABSTRACT FROM AUTHOR]

The design of complex concurrent systems often involves intricate performance and dependability considerations. Continuous-time Markov chains (CTMCs) are a widely used modeling formalism that captures such performance and dependability properties, and makes them analyzable by model checking. In this paper, we focus on time-bounded probabilistic properties of infinite-state CTMCs, expressible in a subset of continuous stochastic logic (CSL). This comprises important dependability measures, such as time-bounded probabilistic reachability, performability, survivability, and various availability measures like instantaneous, conditional instantaneous and interval availabilities. Conventional model checkers explore the given model exhaustively, which is often costly, due to state explosion, and sometimes impossible because the model is infinite. This paper presents a method that only explores the model up to a finite depth. The required depth is determined on the fly by an algorithm that is configurable in order to adapt to the characteristics of different classes of models. We provide experimental evidence showing that our method is effective. [ABSTRACT FROM AUTHOR]

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the collected rewards while interacting with their environment while using some prior knowledge that is accessed beforehand. Many BRL algorithms have already been proposed, but the benchmarks used to compare them are only relevant for specific cases. The paper addresses this problem, and provides a new BRL comparison methodology along with the corresponding open source library. In this methodology, a comparison criterion that measures the performance of algorithms on large sets of Markov Decision Processes (MDPs) drawn from some probability distributions is defined. In order to enable the comparison of non-anytime algorithms, our methodology also includes a detailed analysis of the computation time requirement of each algorithm. Our library is released with all source code and documentation: it includes three test problems, each of which has two different prior distributions, and seven state-of-the-art RL algorithms. Finally, our library is illustrated by comparing all the available algorithms and the results are discussed. [ABSTRACT FROM AUTHOR]

In this paper, exact and approximate approaches for studying queuing models with state-dependent jump priorities are developed. Both models with finite separate buffers and finite common buffer for heterogeneous calls are investigated. It is shown that both models might be described by twodimensional Markov Chains (2-D MC). Exact approach based on solution of appropriate system of balance equations (SBE) for state probabilities faced with big computational challenges for large scale models. To overcome the indicated difficulties an approximate approach based on the state space merging algorithms is developed. This approach allows to construct simple algorithms to calculate the Quality of Service (QoS) metrics of the examined models. The results of numerical experiments are demonstrated. [ABSTRACT FROM AUTHOR]

Abstract: External control of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases. For this purpose, a number of stochastic optimal control approaches have been proposed. Probabilistic Boolean networks (PBNs) as powerful tools for modeling gene regulatory systems have attracted considerable attention in systems biology. In this paper, we deal with a problem of optimal intervention in a PBN with the help of the theory of discrete time Markov decision process. Specifically, we first formulate a control model for a PBN as a first passage model for discrete time Markov decision processes and then find, using a value iteration algorithm, optimal effective treatments with the minimal expected first passage time over the space of all possible treatments. In order to demonstrate the feasibility of our approach, an example is also displayed. [Copyright &y& Elsevier]

Abstract: The probability hypothesis density (PHD) filter is a promising algorithm for multitarget tracking, which can be extended for jump Markov systems (JMS). Since the existing multiple model sequential Monte Carlo PHD (MM SMC-PHD) filter is not interacting, two extensions of the SMC-PHD filters are developed in this paper. The interacting multiple-model (IMM) SMC-PHD filter approximates the model conditional PHD of target states by particles, and performs the interaction by resampling without any a priori assumption of the noise. The IMM Rao-Blackwellized particle (RBP) PHD filter uses the idea of Rao-Blackwellized to further enhance the performance of target state estimation for JMS with mixed linear/nonlinear state space models. The simulation results show that the proposed algorithms have better performances than the existing MM SMC-PHD filter in terms of state filtering and target number estimation. [Copyright &y& Elsevier]

Abstract: Finite source retrial G-queues are good mathematical models of communication systems and networks, so their investigation is important for theory and applications. In this paper, we analyze the MAP/PH/N retrial queue with finite number of sources and MAP arrivals of negative customers operating in a finite state Markovian random environment. The arrival of a negative customer with equal probability goes to any busy server to remove the customer being in service. The multi-dimensional Markov chain describing the behavior of the system is investigated. The algorithms for calculating the stationary state probabilities are elaborated. Main performance measures are obtained. Illustrative numerical examples are presented. [ABSTRACT FROM AUTHOR]

As practitioners we are interested in the likelihood of the population containing a copy of the optimum. The dynamic systems approach, however, does not help us to calculate that quantity. Markov chain analysis can be used in principle to calculate the quantity. However, since the associated transition matrices are enormous even for modest problems, it follows that in practice these calculations are usually computationally infeasible. Therefore, some improvements on this situation are desirable. In this paper, we present a method for modeling the behavior of finite population evolutionary algorithms (EAs), and show that if the population size is greater than 1 and much less than the cardinality of the search space, the resulting exact model requires considerably less memory space for theoretically running the stochastic search process of the original EA than the Nix and Vose-style Markov chain model. We also present some approximate models that use still less memory space than the exact model. Furthermore, based on our models, we examine the selection pressure by fitness-proportionate selection, and observe that on average over all population trajectories, there is no such strong bias toward selecting the higher fitness individuals as the fitness landscape suggests. [ABSTRACT FROM AUTHOR]

This paper deals with the severity of ruin in a discrete semi-Markov risk model. It is shown that the work of Reinhard and Snoussi (Stochastic Models, 18) can be extended to cover the case where the premium is an integer value and no restriction on the annual result is imposed. In particular, it is shown that the severity of ruin without initial surplus is solution of a system of equations. It can be obtained by a monotonically converging algorithm when the claims are bounded. [ABSTRACT FROM AUTHOR]

Objective: There are few analytical results that describe patient compliance with drug administration regimens. The purpose of this paper is to develop and assess stochastic approaches for mathematical modelling of patient compliance with administration regimens.Methods: Two stochastic models based on Markov-dependent random variables and on the Ising model were assessed for their ability to describe the variable nature of drug compliance.Results: Both models use only experimentally accessible data, and their predictions were tested against published clinical compliance data obtained from electronic monitoring devices. The models satisfactorily fitted administration interval distribution data from several patients treated with diltiazem, a calcium channel antagonist, or zidovudine, an antiretroviral agent. The Ising model provides additional analytical expressions for the distribution of success runs and 'drug holidays' in administration regimens. These distribution predictions were tested with success run data for diltiazem and drug holiday data for two nonsteroidal anti-inflammatory drugs, piroxicam and tenoxicam.Conclusions: Stochastic models can provide useful insights into drug compliance, and can be used to identify the administration patterns that are more likely to occur during drug self-administration in populations. [ABSTRACT FROM AUTHOR]