The comparison between microbial sequencing data is critical to understand the dynamics of microbial communities. The alignment-based tools analyzing metagenomic datasets require reference sequences and read alignments. The available alignment-free dissimilarity approaches model the background sequences with Fixed Order Markov Chain (FOMC) yielding promising results for the comparison of microb...

Recently Peres and Shields discovered a new method for estimating the order of a stationary fixed order Markov chain [15]. They showed that the estimator is consistent by proving a threshold result. While this threshold is valid asymptotically in the limit, it is not very useful for DNA sequence analysis where data sizes are moderate. In this paper we give a novel interpretation of the Peres-Sh...

The incidence function approach to modelling of metapopulation dynamics is critically examined both from the biological perspective and for technical issues, the latter by placing the model in the context of Markov random fields and the statistical analysis of binary lattice systems. The claim that the model can be used to estimate time-process parameters from spatialpattern data is examined us...

This article reports on the use of Hidden Markov Models to improve the results of Localization within a sequence of Sensor Views. Local image features (SIFT) and multiple types of features from a 2D laser range scan are all converted into binary form and integrated into a single, binary, Feature Incidence Matrix (FIM). To reduce the large dimensionality of the binary data, it is modeled in term...

Jump Markov linear systems are linear systems whose parameters evolve with time according to a finite-state Markov chain. Given a set of observations, our aim is to estimate the states of the finite-state Markov chain and the continuous (in space) states of the linear system. The computational cost in computing conditional mean or maximum a posteriori (MAP) state estimates of the Markov chain o...

Given a probability law π on a set S and a function g : S → R, suppose one wants to estimate the mean ḡ = ∫ g dπ. The Markov Chain Monte Carlo method consists of inventing and simulating a Markov chain with stationary distribution π. Typically one has no a priori bounds on the chain’s mixing time, so even if simulations suggest rapid mixing one cannot infer rigorous confidence intervals for ḡ. ...

We present a new CuSum chart for monitoring shifts in the mean of autocorrelated data. The monitoring statistic is the plain cumulative sum of differences between the observations and the target value, and the control limits are a function of the asymptotic variance constant and target in-control average run length. Our method uses a variety of non-parametric techniques that are popular in the ...

Standard control charts are often based on the assumption that the observations follow a specific parametric distribution, such as the normal. In many applications we do not have enough information to make this assumption and in such situations, development and application of control charts that do not depend on a particular distributional assumption is desirable. Nonparametric or distribution-...

In this paper we show that an arbitrary tree-like Markov chain can be embedded in a binary tree-like Markov chain with a special structure. When combined with [7], this implies that any tree structured QBD Markov chain can be reduced to a binary tree-like process. Furthermore, a simple relationship between the V , Rs and Gs matrices of the original and the binary tree-like Markov chain is estab...

There are some problems with estimating the time required for the manufacturing process of products, especially when there is a variable serving time, like control stage. These problems will cause overestimation of process time. Layout constraints, reworking constraints and inflexible product schedule in multi product lines need a precise planning to reduce volume in particular situation of lin...