Specialized classifiers, namely those dedicated to a subset of classes, are often adopted in realworld recognition systems. However, integrating such classifiers is nontrivial. Existing methods, e.g. weighted average, usually implicitly assume that all constituents of an ensemble cover the same set of classes. Such methods can produce misleading predictions when used to combine specialized clas...

The deviation matrix of an ergodic, continuous-time Markov chain with transition probability matrix P (.) and ergodic matrix Π is the matrix D ≡ ∫∞ 0 (P (t)−Π)dt. We give conditions for D to exist and discuss properties and a representation of D. The deviation matrix of a birth-death process is investigated in detail. We also describe a new application of deviation matrices by showing that a me...

We develop a reversible jump Markov chain Monte Carlo approach to estimating the posterior distribution of phylogenies based on aligned DNA/RNA sequences under several hierarchical evolutionary models. Using a proper, yet nontruncated and uninformative prior, we demonstrate the advantages of the Bayesian approach to hypothesis testing and estimation in phylogenetics by comparing different model...

A variety of phenomena are best described using dynamical models which operate on a discrete state space and in continuous time. Examples include Markov (and semiMarkov) jump processes, continuous-time Bayesian networks, renewal processes and other point processes. These continuous-time, discrete-state models are ideal building blocks for Bayesian models in fields such as systems biology, genet...

Wireless Sensor Network (WSN) applications, there may be many low-powered sensor nodes which can communicate with each other by wireless techniques. Due to limited power supply, the satisfiability of performability properties, which include energy constraints especially, must be confirmed in design phase. This will help one to avoid implementing impracticable designs. A typical performability p...

We consider a firm that faces random demand and receives product from a single supplier who faces random supply. The supplier’s availability may be affected by events such as storms, strikes, machine breakdowns, and congestion due to orders from its other customers. In our model, we consider a dynamic environment: the probability of disruption, as well as the demand intensity, can be time depen...

In this paper we study time-bounded verification of a finite continuoustime Markov chain (CTMC) C against a real-time specification, provided either as a metric temporal logic (MTL) property φ, or as a timed automaton (TA) A. The key question is: what is the probability of the set of timed paths of C that satisfy φ (or are accepted by A) over a time interval of fixed, bounded length? We provide...

Under interference constraint and energy consumption constraint, to maximize the channel utilization, an opportunistic spectrum access (OSA) strategy for a slotted secondary user (SU) overlaying an unslotted ON/OFF continuous time Markov chain (CTMC) modeled primary network is proposed. The OSA strategy is investigated via a cross-layer optimization approach, with joint consideration of sensing...

A central controller chooses a state-dependent transmission rate for each user in a fading, downlink channel by varying transmission power over time. For each user, the state of the channel evolves over time according to an exogenous continuous-time Markov chain (CTMC), which affects the quality of transmission. The traffic for each user, arriving at the central controller, is modeled as a fini...

We propose an accelerated CTMC simulation method that is exact in the sense that it produces all of the transitions involved. We call our method Path Sampling Simulation as it samples from the distribution of trajectories and the distribution of time given some particular trajectory. Sampling from the trajectory space rather than the transition space means that we need to generate fewer random ...