The Use of Time-Varying Markov Model to Study the Effect of Weather on Asthma

A time-varying Markov model with periodically changing transition probability matrix is proposed to predict the behaviour of those diseases which are periodic with respect to year. In the present paper, computing techniques are used for asthma patients to estimate the limiting state probabilities at the end of each scycle (i.e. seasons), and it has been shown that results agree with the real life situations.     

Gaußsche Markovketten zweiter Ordnung

We find the general form of the proper Gaussian Markov chains of second order and give an example for them. Comparing with the Gaussian Markov processes they can be used as an improved growth model.     

On a modified Markov branching process

We modify a Markov branching process such that each particle produces offspring only if its life length is greater than T. We find exact expressions for the mean number of particles at time t.     

Probability Model for Crop Yield Forecasting

Markov chain approach has been adopted to forecast sugarcane yield. Two years data (1977–78 and 1978–79) on biometrical characters and yield collected by IASRI, New Delhi under the pilot study on pre-harvest forecasting of sugarcane yield in Meerut district (U.P.) were utilised for the study. Yield forecasts at 7–8 months after planting (about 2–3 months before harvest) were very close to the observed ones, per cent deviations from observed being 4% in 1978–79 and 2% in 1977–78. This study thus reveals that Markov Chain method can be successfully used in crop yield forecasting.     

一个改进的马尔科夫模型

离子通道是细胞膜里的大分子孔道,是跨越细胞膜里的蛋白质大分子,是神经、肌肉等细胞膜兴奋性的基础。人体细胞均具有完成特殊功能的离子通道,构建离子通道,尤其其门控行为的动力学模型,对于研究离子通道的相关课题具有重要意义。离子通道的开关反映了蛋白质构象变化的动力学过程。本文介绍了细胞膜离子通道的基本内容和几种常用模型,并根据Markov链对离子通道门控行为的一个二态（闭、开）模型的Markov过程进行了改进,得到了包含失活状态的离子通道门控行为的Markov模型。     

A Sufficient Condition for Reducing Recursions in Hidden Markov Models

In hidden Markov models, the probability of observing a set of strings can be computed using recursion relations. We construct a sufficient condition for simplifying the recursion relations for a certain class of hidden Markov models. If the condition is satisfied, then one can construct a reduced recursion where the dependence on Markov states completely disappears. We discuss a specific example—namely, statistical multiple alignment based on the TKF-model—in which the sufficient condition is satisfied.     

Bayesian regularization of diffusion tensor images

Diffusion tensor imaging (DTI) is a powerful tool in the study of the course of nerve fiber bundles in the human brain. Using DTI, the local fiber orientation in each image voxel can be described by a diffusion tensor which is constructed from local measurements of diffusion coefficients along several directions. The measured diffusion coefficients and thereby the diffusion tensors are subject to noise, leading to possibly flawed representations of the 3-dimensional (3D) fiber bundles. In this paper, we develop a Bayesian procedure for regularizing the diffusion tensor field, fully utilizing the available 3D information of fiber orientation. The use of the procedure is exemplified on synthetic and in vivo data.     

Estimating Variable Effective Population Sizes from Multiple Genomes: A Sequentially Markov Conditional Sampling Distribution Approach

Throughout history, the population size of modern humans has varied considerably due to changes in environment, culture, and technology. More accurate estimates of population size changes, and when they occurred, should provide a clearer picture of human colonization history and help remove confounding effects from natural selection inference. Demography influences the pattern of genetic variation in a population, and thus genomic data of multiple individuals sampled from one or more present-day populations contain valuable information about the past demographic history. Recently, Li and Durbin developed a coalescent-based hidden Markov model, called the pairwise sequentially Markovian coalescent (PSMC), for a pair of chromosomes (or one diploid individual) to estimate past population sizes. This is an efficient, useful approach, but its accuracy in the very recent past is hampered by the fact that, because of the small sample size, only few coalescence events occur in that period. Multiple genomes from the same population contain more information about the recent past, but are also more computationally challenging to study jointly in a coalescent framework. Here, we present a new coalescent-based method that can efficiently infer population size changes from multiple genomes, providing access to a new store of information about the recent past. Our work generalizes the recently developed sequentially Markov conditional sampling distribution framework, which provides an accurate approximation of the probability of observing a newly sampled haplotype given a set of previously sampled haplotypes. Simulation results demonstrate that we can accurately reconstruct the true population histories, with a significant improvement over the PSMC in the recent past. We apply our method, called diCal, to the genomes of multiple human individuals of European and African ancestry to obtain a detailed population size change history during recent times.     

The SMC′ Is a Highly Accurate Approximation to the Ancestral Recombination Graph

Two sequentially Markov coalescent models (SMC and SMC′) are available as tractable approximations to the ancestral recombination graph (ARG). We present a Markov process describing coalescence at two fixed points along a pair of sequences evolving under the SMC′. Using our Markov process, we derive a number of new quantities related to the pairwise SMC′, thereby analytically quantifying for the first time the similarity between the SMC′ and the ARG. We use our process to show that the joint distribution of pairwise coalescence times at recombination sites under the SMC′ is the same as it is marginally under the ARG, which demonstrates that the SMC′ is, in a particular well-defined, intuitive sense, the most appropriate first-order sequentially Markov approximation to the ARG. Finally, we use these results to show that population size estimates under the pairwise SMC are asymptotically biased, while under the pairwise SMC′ they are approximately asymptotically unbiased.     

Multi-Stage Markov Analysis of Progressive Disease Applied to Melanoma

A fundamental research goal in clinical studies of progressive, multi-stage disease is to understand its natural history and its relationship with prognostic factors. Our current understanding of this topic is based on the use of two-stage methods for event-time analysis which neglect intermediate transition information. In contrast, a multi-stage model utilizes all available data and provides more accurate insight into disease progression. We specify a forward-flowing multi-stage Markov model based on the discrete clinical stages of disease. By assuming the process to be Markovian, we avoid unnecessary complications to our numerical estimation procedure. Due to noncontinuous patient monitoring and the chronic nature of progressive disease, heavy right- and interval-censoring exists in the transition data. We develop a modified ECM algorithm to numerically carry out the otherwise complicated parameter estimation for this process. We also identify significant prognostic factors relevant to each transition, along with the relative importance of each prognostic factor. The numerical estimation is stable, and the parameter estimates are maximum likelihood estimates (Meng, 1990). In general our forward-flowing multi-stage models provide a flexible framework for the study of the effects of prognostic factors on progression among several stages. We apply our Markov model to a dataset of malignant melanoma patients, and present an inferential discussion. Results from our multi-stage Markov model provide an improved understanding of melanoma progression.     

Markov chain Monte Carlo fitting of single-channel data from inositol trisphosphate receptors

In many cell types, the inositol trisphosphate receptor (IPR) is one of the important components that control intracellular calcium dynamics, and an understanding of this receptor (which is also a calcium channel) is necessary for an understanding of calcium oscillations and waves. Recent advances in experimental techniques now allow for the measurement of single-channel activity of the IPR in conditions similar to its native environment, and these data can be used to determine the rate constants in Markov models of the IPR. We illustrate a parameter estimation method based on Markov chain Monte Carlo, which can be used to fit directly to single-channel data, and determining, as an intrinsic part of the fit, the times at which the IPR is opening and closing. We show, using simulated data, the most complex Markov model that can be unambiguously determined from steady-state data and show that non-steady-state data is required to determine more complex models.     

Analyses of genetic ancestry enable key insights for molecular ecology

Gene flow and recombination in admixed populations produce genomes that are mosaic combinations of chromosome segments inherited from different source populations, that is, chromosome segments with different genetic ancestries. The statistical problem of estimating genetic ancestry from DNA sequence data has been widely studied, and analyses of genetic ancestry have facilitated research in molecular ecology and ecological genetics. In this review, we describe and compare different model‐based statistical methods used to infer genetic ancestry. We describe the conceptual and mathematical structure of these models and highlight some of their key differences and shared features. We then discuss recent empirical studies that use estimates of genetic ancestry to analyse population histories, the nature and genetic basis of species boundaries, and the genetic architecture of traits. These diverse studies demonstrate the breadth of applications that rely on genetic ancestry estimates and typify the genomics‐enabled research that is becoming increasingly common in molecular ecology. We conclude by identifying key research areas where future studies might further advance this field.     

Bayesian analysis of discrete survival data with a hidden Markov chain

This paper considers the discrete survival data from a Bayesian point of view. A sequence of the baseline hazard functions, which plays an important role in the discrete hazard function, is modeled with a hidden Markov chain. It is explained how the resultant model is implemented via Markov chain Monte Carlo methods. The model is illustrated by an application of real data.     

Likelihood-ratio tests for hidden Markov models

We consider hidden Markov models as a versatile class of models for weakly dependent random phenomena. The topic of the present paper is likelihood-ratio testing for hidden Markov models, and we show that, under appropriate conditions, the standard asymptotic theory of likelihood-ratio tests is valid. Such tests are crucial in the specification of multivariate Gaussian hidden Markov models, which we use to illustrate the applicability of our general results. Finally, the methodology is illustrated by means of a real data set.     

Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo

This article presents a statistical method for detecting recombination in DNA sequence alignments, which is based on combining two probabilistic graphical models: (1) a taxon graph (phylogenetic tree) representing the relationship between the taxa, and (2) a site graph (hidden Markov model) representing interactions between different sites in the DNA sequence alignments. We adopt a Bayesian approach and sample the parameters of the model from the posterior distribution with Markov chain Monte Carlo, using a Metropolis-Hastings and Gibbs-within-Gibbs scheme. The proposed method is tested on various synthetic and real-world DNA sequence alignments, and we compare its performance with the established detection methods RECPARS, PLATO, and TOPAL, as well as with two alternative parameter estimation schemes.     

Estimating rate constants from single ion channel currents when the initial distribution is known

Single ion channel currents can be analysed by hidden or aggregated Markov models. A classical result from Fredkin et al. (Proceedings of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer, vol I, pp 269–289, 1985) states that the maximum number of identifiable parameters is bounded by 2n_on_c, where n_o and n_c denote the number of open and closed states, respectively. We show that this bound can be overcome when the probabilities of the initial distribution are known and the data consist of several sweeps.     

A stochastic repair—misrepair model for the effects of radiation on cells

The purpose of this paper is to present a stochastic model for effects of radiation on cells. The model includes provisions for lethal effects and for transformation to malignant status. Empirical findings indicate the presence of some repair mechanism in the cells, and a way to incorporate this idea into the model is discussed. Comparisons of the derived survival and transformation probabilities with actual data are favorable. The interpretation of the estimated values leads to questions to be investigated by the experimenters.Supported in part by the Graduate Research Board, University of Maryland     

A multistate Markov chain model for evaluating a sterilization policy

"A multistate Markov chain model corresponding to varying fertility and mortality rates at different levels of surviving children of a couple was developed. Asymptotic probabilities of having a fixed number of children have been worked out." The implied geographical focus is on India.