Production and prediction of land-use/land cover changes (LULCC) map are among the significant issues regarding input of many environmental and hydrological models. Among various introduced methods, similarity-weighted instance-based machine learning algorithm (SimWeight) and Markov-chain with lower complexity and proper performnce are frequently used. The main aim of this study is utilizing Si...

Growing amount of information on biological sequences has made application of statistical approaches necessary for modeling and estimation of their functions. In this paper, sensitivity and specificity of the first and second Markov chains for prediction of genes was evaluated using the complete double stranded  DNA virus. There were two approaches for prediction of each Markov Model parameter,...

The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP...

We present an unsupervised segmentation algorithm comprising an annealing process to select the maximum a posteriori (MAP) realization of a Hierarchical Markov Random Field (MRF) Model. The algorithm consists of a sampling framework which unifies the processes of model selection, parameter estimation and image segmentation, in a single Markov Chain. To achieve this, Reversible Jumps are incorpo...

this paper proposes an extension of markov chain model to characterize of geological formations and to make conditioning on any number of well data possible. in this study, lithofacies cycles of the amiran formation at type and pirshamsedin sections are examined by markov method and entropy analysis as well as field observations, and depositional environment has interpreted by proposed ideal se...

this paper is devoted to the study of determining optimal process mean in system production with the two markets for the sale of goods. in this paper, we developed an absorbing markov chain model in production systems where all items are inspected %100 for conformance with their specification limits. when the value of the quality characteristic of an item falls below a lower limit, the item is ...

An iterative stochastic algorithm to perform maximum a posteriori parameter estimation of hidden Markov models is proposed. It makes the most of the statistical model by introducing an artiicial probability model based on an increasing number of the unobserved Markov chain at each iteration. Under minor regularity assumptions, we provide suucient conditions to ensure global convergence of this ...

It is an important and a complicated task to investigate the convergence of a new genetic algorithm based on quantum mechanics concepts including qubits and superposition of states, namely Quantum Genetic Algorithm, in the field of evolutionary computation. This paper analyzes convergence property of quantum genetic algorithm which uses its special quantum operator instead of canonical operator...