ライフサイエンスコーパス: Markov chain

1 nce relationships as a single finite ergodic Markov chain.

2 nsecutive state sequence was a heterogeneous Markov chain.

3 riate normal across loci using a Monte Carlo Markov chain.

4 uted IP3Rs, each represented by a four-state Markov chain.

5 e hidden states evolve along the genome as a Markov chain.

6 ithout good bounds on the mixing time of the Markov chain.

7 hannel flux were examined using finite-state Markov chains.

8 edictive model of musical structure based on Markov chains.

9 y, based on the concept of spatial absorbing Markov chains.

10 on the theory of continuous-time homogeneous Markov Chains.

11 (gj) records, we transformed an S36SM into a Markov chain 36-state model (MC36SM) of GJ channel gatin

12 ion indicates that the subsampling bootstrap Markov chain algorithm substantially reduces computation

13 We implement a Monte Carlo Markov chain algorithm to perform inference under this m

14 n occur in accordance with a continuous time Markov Chain along the branches of a phylogenetic tree a

15 xponential of the underlying continuous-time Markov chain also show promise, especially in view of re

16 Dirichlet multinomial mixture modeling, Markov chain analysis, and mixed-effect models were used

17 states from long random trajectories on the Markov chain and compare these with the rank of the pres

18 ise the analysis, to track the status of the Markov chain and to save the results.

19 simulation, construction of continuous-time Markov chains and various export formats which allow mod

20 based approach is built on a continuous time Markov chain, and it is capable of evaluating the state

21 We developed a continuous-time Markov chain approach, based on the observation that cha

22 Permeability fields are generated by a Markov Chain approach, which represent facies architectu

23 A theoretical analysis based on microscopic Markov-chain approach is presented to explain the numeri

24 The transitions of the Markov chain are generated using min-cut computations on

25 long the lines of optimal prediction for the Markov chains associated with the dynamics on these netw

26 use linkage disequilibrium and a high-order Markov chain-based algorithm for inference.

27 sent an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models

28 ever, readers should be aware that other non-Markov-chain-based methods are currently in active devel

29 wever, Bayesian models do not always require Markov-chain-based methods for parameter estimation.

30 perience confirms that students WANT to know Markov chains because they hear about them from bioinfor

31 We assume nothing about the Markov chain beyond reversibility and show that signific

32 The method avoids the use of a Monte Carlo Markov chain by employing priors for which the likelihoo

33 The samples from the Markov chain can be summarized in several ways, and new

34 twork models, interpreted as continuous-time Markov chains, can be distinguished from each other unde

35 ce was computed for the distance between two Markov chains, constructed from the transition matrices

36 In this work, a continuous-time Markov chain (CTMC) model is formulated to investigate p

37 ential equations (ODE) and a continuous-time Markov chain (CTMC) model, are developed for spread of h

38 of control theory via the design of optimal Markov chain decision processes, mainly in the framework

39 nd use them to show that the continuous-time Markov chain describing allele frequency change with exc

40 other measures of complexity associated with Markov chain dynamical systems models of progression.

41 els were built using a series of interlinked Markov chains, each representing age increments of the N

42 usted transition probability matrix for this Markov chain enables the calculation of eigenvector valu

43 el the background sequences with Fixed Order Markov Chain (FOMC) yielding promising results for the c

44 are discrete binding events are modeled by a Markov chain for the encounter of small targets by few B

45 We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space e

46 re a dynamical solution concept based on the Markov chain formalism, Conley's Fundamental Theorem of

47 WKB theory and directly treat the underlying Markov chain (formulated as a birth-death process) obeye

48 By first constructing a Markov chain from the transitions between these syllable

49 parameters were inferred based on an ergodic Markov chain generated by our Gibbs sampler.

50 WQuadv1C BeadChip array and imputed with the Markov Chain Haplotyping algorithm using the HapMap 3 re

51 We propose the use of subsampling bootstrap Markov chain in genomic prediction.

52 We show that when the Markov chain is lumpable, we recover the partition with

53 A Markov chain (MC) model recapitulating wild type (WT) an

54 given through modeling the DNA sequence as a Markov chain (MC).

55 onentially with the increase of the order of Markov Chain (MC).

56 osed-form solutions, we employ a Monte Carlo Markov Chain (MCMC) approach to perform classification.

57 We implement a Monte Carlo Markov chain (MCMC) based algorithm that simultaneously

58 thods (classical and inverse), a Monte Carlo Markov Chain (MCMC) estimation was used to generate sing

59 We then implement a Monte Carlo Markov Chain (MCMC) procedure for simultaneous sampling

60 time series was evaluated with a Monte Carlo Markov Chain (MCMC) sampling procedure.

61 We used Monte-Carlo Markov Chain (MCMC) techniques to parameterize our model

62 del accounting for UH in all vital rates and Markov chain methods to calculate demographic outcomes.

63 rocess, the TKF91 model is a continuous-time Markov chain model composed of insertion, deletion, and

64 The proposed Markov chain model consists of the regulatory core and t

65 We illustrate the approach using a simple Markov chain model to capture sequential dependencies be

66 from 1985 to 2011 for 598 216 adults, into a Markov chain model to estimate remaining lifetime diabet

67 ired fish of varying boldness, and we used a Markov Chain model to infer the individual rules underly

68 We fit a Markov chain model to ~20,000 macroinvertebrate samples

69 The proposed Markov chain model was shown to approximate the progress

70 A four-state Markov chain model was used to quantify the rate constan

71 We base our method on an arbitrary-order Markov chain model with community structure, and develop

72 Here, we introduce a Markov chain model with each state corresponding to an a

73 astic compartmental model (a continuous time Markov chain model) with both horizontal and vertical tr

74 eviation with respect to the continuous time Markov chain model, and we show that the new approach is

75 s of connectivity in both modalities using a Markov chain model-based approach.

76 ne sets as random samples from a first-order Markov chain model.

77 DNA: a model based on statistical physics, a Markov-chain model and a computational simulation.

78 mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively pre

79 Using a Markov-chain model to infer the individual rules underly

80 fects modeling, medoid-based clustering, and Markov chain modeling were used to analyze community tem

81 We present a discrete Markov chains modelling framework that deals with the lo

82 A methodology based on Markov chain models and network analytic metrics can hel

83 In this article, Markov chain models of Ca(2+) release sites are used to

84 f puffs and sparks, we formulate and analyze Markov chain models of Ca(2+) release sites composed of

85 the thermodynamic entropy production rate of Markov chain models of puffs and sparks.

86 When Markov chain models of these intracellular Ca(2+)-regula

87 change to species dynamics via multispecies Markov chain models reveals strong links between in situ

88 (MSCE) models are a class of continuous-time Markov chain models that capture the multi-hit initiatio

89 ers was estimated using the "slice sampling" Markov Chain Monte Carlo (MCMC) algorithm implemented in

90 A trans-dimensional Markov Chain Monte Carlo (MCMC) algorithm is used to eff

91 cally accounts for missing values based on a Markov chain Monte Carlo (MCMC) algorithm that incorpora

92 In this article, we propose a Markov chain Monte Carlo (MCMC) algorithm, HASH (haploty

93 rly designed for Bayesian analysis using the Markov chain Monte Carlo (MCMC) algorithm.

94 heir variances, and then solved by using the Markov chain Monte Carlo (MCMC) algorithm.

95 Markov chain Monte Carlo (MCMC) algorithms are developed

96 We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performin

97 decreased computational cost relative to the Markov chain Monte Carlo (MCMC) algorithms that have gen

98 ds for summarizing the results of a Bayesian Markov chain Monte Carlo (MCMC) analysis of population s

99 thogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate tim

100 We here propose a Markov chain Monte Carlo (MCMC) approach using an adapti

101 of self-seeding of primary tumors, we use a Markov chain Monte Carlo (MCMC) approach, based on large

102 abolism and proposes to use the results of a Markov chain Monte Carlo (MCMC) based flux balance analy

103 We applied Markov Chain Monte Carlo (MCMC) for parameter estimation

104 Statistical modeling applying a Bayesian Markov chain Monte Carlo (MCMC) framework to the environ

105 We used the Markov chain Monte Carlo (MCMC) implemented Bayesian met

106 nrichment measurement methods by combining a Markov chain Monte Carlo (MCMC) matrix factorization alg

107 ctures of shales are reconstructed using the markov chain monte carlo (MCMC) method based on scanning

108 correlation in exon splicing patterns, and a Markov chain Monte Carlo (MCMC) method coupled with a si

109 describe a coalescent-based full-likelihood Markov chain Monte Carlo (MCMC) method for jointly estim

110 resent a new C implementation of an advanced Markov chain Monte Carlo (MCMC) method for the sampling

111 to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast agg

112 Using a Bayesian Markov chain Monte Carlo (MCMC) method, the divergence t

113 tic parameters were then estimated using the Markov chain Monte Carlo (MCMC) method.

114 with many markers can only be evaluated with Markov chain Monte Carlo (MCMC) methods that are slow to

115 ting process, which was implemented by using Markov chain Monte Carlo (MCMC) methods, significantly r

116 eter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods.

117 g Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) methods.

118 Markov chain Monte Carlo (MCMC) or the Metropolis-Hastin

119 We use a Markov chain Monte Carlo (MCMC) procedure to sample from

120 ally, MACAU uses a computationally expensive Markov Chain Monte Carlo (MCMC) procedure, which cannot

121 We describe a Bayesian Markov chain Monte Carlo (MCMC) sampler for protein mult

122 sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algor

123 archies using a combination of heuristic and Markov chain Monte Carlo (MCMC) sampling procedures and

124 from sequence data using Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling, which is widel

125 lux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limi

126 ity using the profile likelihood method, the Markov chain Monte Carlo (MCMC) technique, and the exten

127 r widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally use

128 The method uses Markov Chain Monte Carlo (MCMC) to approximate the poste

129 elop a Bayesian full-likelihood method using Markov Chain Monte Carlo (MCMC) to estimate background r

130 Inference using reversible-jump Markov chain Monte Carlo (MCMC) to model the placement a

131 ariables and sampled via Metropolis-Hastings Markov chain Monte Carlo (MCMC), enabling systematic sta

132 riables from the posterior distribution with Markov Chain Monte Carlo (MCMC), using the recently prop

133 f Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC).

134 n via iterated filtering (MIF), and particle Markov chain Monte Carlo (pMCMC)--and three ensemble fil

135 , and a parallel computing algorithm for the Markov chain Monte Carlo -based posterior inference and

136 erated through the publicly available method Markov chain Monte Carlo 5C (MCMC5C) illustrated the out

137 A dynamic iteratively reweighted Markov chain Monte Carlo algorithm conveniently recycles

138 d the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed li

139 Our Markov chain Monte Carlo algorithm represents a general

140 We develop a new hybrid Markov Chain Monte Carlo algorithm that combines the Gib

141 We developed a Gibbs sampling Markov chain Monte Carlo algorithm that produces posteri

142 Data were analyzed with a Markov chain Monte Carlo algorithm to model transmission

143 It applies a Markov chain Monte Carlo algorithm to sample from a join

144 ce under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted netw

145 We use a Markov Chain Monte Carlo algorithm with Multiple Markov

146 The approach uses a Markov chain Monte Carlo algorithm, allowing inference w

147 Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the

148 orithm, the Elston-Stewart algorithm and the Markov chain Monte Carlo algorithm.

149 Model parameters are estimated using a Markov chain Monte Carlo algorithm.

150 with the help of a Bayesian reversible jump Markov chain Monte Carlo algorithm.

151 probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm.

152 Bayes to improve the convergence rate of the Markov Chain Monte Carlo algorithm.

153 r involve computationally intensive Bayesian Markov chain Monte Carlo algorithms that do not scale we

154 ecause of the computational demands of using Markov Chain Monte Carlo algorithms to estimate paramete

155 BP algorithm compares in quality with exact Markov Chain Monte Carlo algorithms, yet BP is far super

156 using a random effects model estimated using Markov Chain Monte Carlo algorithms.

157 n framework and inference is performed using Markov chain Monte Carlo algorithms.

158 m (not known a priori) and are sampled using Markov chain Monte Carlo algorithms.

159 a rank was assigned to each treatment after Markov Chain Monte Carlo analyses to create a surface un

160 Bayesian Markov chain Monte Carlo analysis suggests a mean recent

161 ores a data integration methodology based on Markov chain Monte Carlo and simulated annealing.

162 intractable and approximate methods such as Markov chain Monte Carlo and Variational Bayes (VB) are

163 We model the inferred deformation using a Markov chain Monte Carlo approach to solve for change in

164 tial agent-based model was calibrated with a Markov chain Monte Carlo approach.

165 over the space of phylogenetic trees using a Markov Chain Monte Carlo approach.

166 ficantly more computationally efficient than Markov Chain Monte Carlo approaches.

167 on (LS) and Approximate Bayesian Computation Markov chain Monte Carlo estimation (ABC-MCMC), to infer

168 ed using a Bayesian inferential approach and Markov chain Monte Carlo estimation methods.

169 Parameter estimation is carried out using a Markov chain Monte Carlo expectation-maximization (MCMC-

170 We have implemented Metropolis-Hastings Markov Chain Monte Carlo for optimizing primer reuse.

171 stochastic clock network ensemble fitted by Markov Chain Monte Carlo implemented on general-purpose

172 d phylogenies reconstructed through Bayesian Markov chain Monte Carlo inference indicated that these

173 um-likelihood trees and dated using Bayesian Markov Chain Monte Carlo inference.

174 tation involves imputation steps within each Markov chain Monte Carlo iteration and Monte Carlo integ

175 By using Bayesian Markov chain Monte Carlo joint oligogenic linkage and as

176 nt and recessive models was performed by the Markov chain Monte Carlo linkage analysis method, MCLINK

177 A Markov chain Monte Carlo mathematical approach can deter

178 stimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform B

179 less, we successfully implemented a two-step Markov chain Monte Carlo method that we called "BICME",

180 A mathematical model applying a Markov Chain Monte Carlo method to estimate probability

181 We use a reversible-jump Markov chain Monte Carlo method to reconstruct shifts in

182 For parameter estimation, we use a modern Markov Chain Monte Carlo method which allows full uncert

183 were analyzed using Bayesian reasoning and a Markov chain Monte Carlo method with a set of simultaneo

184 The present study used a Markov Chain Monte Carlo method, with knowledge about th

185 and evolutionary parameters by applying the Markov chain Monte Carlo method.

186 etwork meta-analysis was conducted using the Markov chain Monte Carlo method.

187 Markov chain Monte Carlo methods (MCMC) are essential to

188 Of the various fitting methods, Markov Chain Monte Carlo methods are common.

189 Markov chain Monte Carlo methods estimated extra-Poisson

190 an settings using the lme4 package in R, and Markov chain Monte Carlo methods in WinBUGS.

191 derlying assumption for many of the proposed Markov Chain Monte Carlo methods is that the data repres

192 inferred using slow sampling methods such as Markov Chain Monte Carlo methods or faster gradient base

193 yesian framework using data augmentation and Markov chain Monte Carlo methods to estimate variation i

194 A Bayesian approach using Markov chain Monte Carlo methods was applied for an appr

195 Markov chain Monte Carlo methods were used to estimate t

196 In the context of inferences employing Markov chain Monte Carlo methods, the accuracy of the ma

197 ing particle filtering methods with Bayesian Markov chain Monte Carlo methods, we are able to fit a w

198 Using particle Markov chain Monte Carlo methods, we fitted to gonorrhea

199 nd myocardial infarction was determined from Markov chain Monte Carlo methods.

200 and calculated 95% credible intervals using Markov Chain Monte Carlo methods.

201 ihood, and we searched parameter space using Markov chain Monte Carlo methods.

202 e estimated in a Bayesian framework by using Markov chain Monte Carlo methods.

203 to HWE was evaluated by an exact test using Markov Chain Monte Carlo methods.

204 We then developed a Markov chain Monte Carlo model of recurrent UTI for each

205 We call it the Markov Chain Monte Carlo Optimized Degenerate Primer Reu

206 ble desktop application that uses a Bayesian Markov chain Monte Carlo procedure to estimate the poste

207 time series of flour beetles, we found that Markov chain Monte Carlo procedures for fitting mechanis

208 With finite Markov chain Monte Carlo run lengths, the harmonic mean

209 gins and automated the process of setting up Markov Chain Monte Carlo runs for RNA alignments in Stat

210 ach samples inheritance vectors (IVs) from a Markov Chain Monte Carlo sampler by conditioning on geno

211 hood can be explored using a straightforward Markov chain Monte Carlo sampler, but one further post-p

212 ferred relative expression is represented by Markov chain Monte Carlo samples from the posterior prob

213 etric Bayesian clustering methods, efficient Markov Chain Monte Carlo sampling and novel subsampling

214 I then describe Markov chain Monte Carlo sampling and, in particular, di

215 h a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess eac

216 Further, we apply Markov Chain Monte Carlo sampling method to fit our mode

217 Markov chain Monte Carlo sampling methods often suffer f

218 he principle of the cross-entropy method and Markov chain Monte Carlo sampling techniques.

219 tional regression-based model emulation with Markov Chain Monte Carlo sampling to calibrate three sel

220 These Bayesian methods (with the aid of Markov chain Monte Carlo sampling) provide a generalizab

221 To estimate the model parameters we use Markov chain Monte Carlo sampling.

222 d estimation or via Bayesian inference using Markov chain Monte Carlo sampling.

223 h parameters are estimated from the data via Markov chain Monte Carlo sampling.

224 e fit the model using Bayesian inference and Markov chain Monte Carlo simulation to successive snapsh

225 random-effects models using vague priors and Markov chain Monte Carlo simulation with Gibbs sampling,

226 ic contact patterns for the countries, using Markov chain Monte Carlo simulation.

227 Here, we describe an approach in which Markov chain Monte Carlo simulations are used to integra

228 to apply the Bayesian approach executed with Markov chain Monte Carlo simulations using two data sets

229 hift information in a probabilistic model in Markov chain Monte Carlo simulations.

230 exity (PLEX) is a flexible and fast Bayesian Markov chain Monte Carlo software program for large-scal

231 A Markov chain Monte Carlo technique was developed to solv

232 ectively, based on the observations with the Markov chain Monte Carlo technique.

233 nting for these features directly and employ Markov chain Monte Carlo techniques to provide robust in

234 oach to meta-regression analysis, which uses Markov chain Monte Carlo techniques, to assess the relat

235 BELT uses Markov chain Monte Carlo to directly sample maximum-entr

236 ted models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior

237 We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distrib

238 le-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the em

239 g bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population

240 The model is fitted via Markov chain Monte Carlo with data augmentation to snaps

241 The preferred model (Bayesian Markov chain Monte Carlo) accounted for missing data, se

242 to Robust Estimates of ALelle frequency, via Markov chain Monte Carlo, and Complexity Of Infection us

243 zed model, the inference algorithms, such as Markov chain Monte Carlo, reliably and quickly find the

244 then used Bayesian inference, in the form of Markov chain Monte Carlo, to learn model parameter distr

245 This is enabled by a Markov Chain Monte Carlo-based maximization process, exe

246 Inference is performed using Markov chain Monte Carlo.

247 subsampling observations in each round of a Markov Chain Monte Carlo.

248 ariety of mechanisms by employing Metropolis Markov chain Monte Carlo.

249 l parameters is stochastically sampled using Markov chain Monte Carlo.

250 The web server implements a Markov Chain Monte-Carlo algorithm with simulated anneal

251 We applied a Markov Chain Monte-Carlo simulation method to impute mis

252 Bayesian Markov chains Monte Carlo and associated time to most re

253 hylogenetic reconstruction, using a Bayesian Markov-chain Monte Carlo approach; (2) evaluation of vir

254 on as a prior probability distribution for a Markov-chain Monte Carlo evaluation of the posterior for

255 an improved variant of a recently published Markov-chain Monte Carlo method is presented.

256 generated from a nonidentifiable model, the Markov-chain Monte Carlo results recover much more infor

257 vations were statistically generated using a Markov-Chain Monte Carlo sampling.

258 We use epidemiological models, Bayesian Markov-chain Monte Carlo, and advanced spatial statistic

259 ion in heterogeneous landscapes and Bayesian Markov-chain-Monte-Carlo inference to estimate dispersal

260 to reduce the state space of the underlying Markov chain of a PBN based on a criterion that the redu

261 x local operator, such as the generator of a Markov chain on a large network, a differential operator

262 under a simple population dynamics model, a Markov chain on the fold network is constructed, and the

263 onstructing and sampling from a finite-state Markov chain on the proposed points such that the overal

264 e present a practical method for simplifying Markov chains on a potentially large state space when de

265 this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its

266 Others based on Monte Carlo Markov Chain or alternative heuristics not only offer no

267 dure can be interpreted as a continuous time Markov chain over a continuum of states.

268 The process is modelled using a Markov chain over the possible structures.

269 The Markov chain prediction model was used for the analysis

270 ape connectivity, and that spatial absorbing Markov chains provide a generalisable and powerful frame

271 We construct a Markov-chain representation of the surface-ocean Lagrang

272 s of 2-component mixtures of continuous-time Markov chains, representing two sub-populations with dis

273 ov Chain Monte Carlo algorithm with Multiple Markov Chain sampling to model local reconnection on 491

274 and time-resolved emission measurements and Markov chain simulations, we show that YO-to-YO resonanc

275 It then derives a Markov-chain state molecular kinetic scheme uniquely ass

276 ylo-grammars, probabilistic models combining Markov chain substitution models with stochastic grammar

277 following: Given the presented state in the Markov chain, take a random walk from the presented stat

278 sis such as solution of scalar equations and Markov chain techniques, as well as numerical simulation

279 an observed version of the unobserved hidden Markov chain that generates one of the two interacting p

280 transition of each parcel is described by a Markov chain that incorporates the successional dynamics

281 ed association rules constituting an ergodic Markov chain, the overall most important rules in the it

282 We used Markov chain theory and maximum likelihood estimation to

283 Using Markov chain theory, we obtain analytical expressions fo

284 intervening in their long-run behavior using Markov chain theory.

285 y, and analyze the single-complex model with Markov chain theory.

286 ult in inoculation into people and applied a Markov chain to estimate the number of severe adverse ev

287 We used a Markov chain to model the probabilities of mutation and

288 Here we introduce the Multiplex Markov chain to quantify correlations in edge dynamics f

289 ve several powerful algorithms, ranging from Markov Chains to message passing to gradient descent pro

290 landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive g

291 represented as a bipartite network, to which Markov chain updates (switching-steps) are applied.

292 al flows and transition probabilities of the Markov chain, verified against computational fluid dynam

293 quences with the data-driven Variable Length Markov Chain (VLMC) in metatranscriptomic data.

294 etect that a presented state of a reversible Markov chain was not chosen from a stationary distributi

295 A state transition model (Markov chain) was developed to evaluate the response of

296 f probabilistic Boolean networks is a finite Markov chain, we define the network sensitivity based on

297 given a value function for the states of the Markov chain, we would like to show rigorously that the

298 n is the large state space of the underlying Markov chain, which poses a serious computational challe

299 nd modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will o

300 amics are modeled according to a first-order Markov chain, with containment represented as an absorbi