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Laws of Large Numbers for Non-Homogeneous Markov Systems with Arbitrary Transition Probability Matrices
In the present we establish a law of large numbers for non-homogeneous Markov systems (NHMS), for which the inherent non-homogeneous Markov chain has...
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Discrete-Time Markov Chain
Markov chains serve as one of the most important methods in the application of probability theory to real-world models involving uncertainty. Markov...
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Bayesian Hidden Markov Models for Early Warning
We show how Bayesian hidden Markov models may be employed to build early warning systems of particular risky events. The adopted model formulation...
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On the Derivative Counting Processes of First- and Second-order Aggregated Semi-Markov Systems
In this paper, first- and second-order discrete-time semi-Markov systems are considered with their finite state space divided into three subsets as...
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hhsmm: an
R package for hidden hybrid Markov/semi-Markov modelsThis paper introduces the hhsmm
R package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov...
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Effects of Prioritized Input on Human Resource Control in Departmentalized Markov Manpower Framework
In this paper, extended Markov manpower models are formulated by incorporating a new class of members of a departmentalized manpower system in a...
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Trend and cycle decomposition of Markov switching (co)integrated time series
In this paper we derive the Beveridge–Nelson (BN) decomposition and the state space representation for various multivariate (co)integrated time...
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Hidden Markov and Semi-Markov Models When and Why are These Models Useful for Classifying States in Time Series Data?
Hidden Markov models (HMMs) and their extensions have proven to be powerful tools for classification of observations that stem from systems with...
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Markov models for duration-dependent transitions: selecting the states using duration values or duration intervals?
In a Markov model the transition probabilities between states do not depend on the time spent in the current state. The present paper explores two...
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On the estimation of partially observed continuous-time Markov chains
Motivated by the increasing use of discrete-state Markov processes across applied disciplines, a Metropolis–Hastings sampling algorithm is proposed...
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Hidden Markov model with missing emissions
In a Hidden Markov model (HMM), from hidden states, the model generates emissions that are visible. Generally, the problems to be solved by such...
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Multivariate Hidden Markov Models
This chapter provides three extended example analyses, applying hidden Markov models to multivariate time series. The first example (Sect. 6.1)...
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High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields
The conditional extremes framework allows for event-based stochastic modeling of dependent extremes, and has recently been extended to spatial and...
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Markov Processes for the Management of a Microgrid
In this work, a stochastic modelization of wind and photovoltaic power productions is coupled with a two-level optimization in which the operating...
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Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models
We consider continuous-time Markov chains that describe the stochastic evolution of a dynamical system by a transition-rate matrix Q which depends on...
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Moments Computation for General Markov Fluid Models
This paper derives new algorithms for the computation of moments in general Markov fluid models. The first one is recursive: the n th moment is...
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Voting Rights, Markov Chains, and Optimization by Short Bursts
Finding outlying elementsin probability distributions can be a hard problem. Taking a real example from Voting Rights Act enforcement, we consider...
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Using Semi-Markov Chains to Solve Semi-Markov Processes
This article provides a novel method to solve continuous-time semi-Markov processes by algorithms from discrete-time case, based on the fact that the...
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Robust parametric inference for finite Markov chains
We consider the problem of statistical inference in a parametric finite Markov chain model and develop a robust estimator of the parameters defining...
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On coregionalized multivariate Gaussian Markov random fields: construction, parameterization, and Bayesian estimation and inference
Gaussian Markov random fields (GMRF) and their multivariate extensions (MGMRFs) are powerful tools for modelling probabilistic interactions of directly...
