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

6 s the programming and run performance of the Markov chain.

7 constants of an arbitrary, discrete, finite Markov chain.

8 te of convergence of many of the widely used Markov chains.

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

10 hannel flux were examined using finite-state 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 sent an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models

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

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

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

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

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

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

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

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

35 A continuous-time Markov chain (CTMC) model is formulated for an influenza

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

60 A Markov chain model (MCM) with two states, shedding and n

61 This work demonstrates that the Markov Chain model captures the essence of the complex m

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

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

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

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

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

67 Here we developed a Markov Chain model to simplify the computation.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

86 We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibb

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

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

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

90 Markov chain Monte Carlo (MCMC) algorithms are developed

91 It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluatin

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

93 Markov chain Monte Carlo (MCMC) algorithms play a critic

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

95 e transitions, commonly used in phylogenetic Markov chain Monte Carlo (MCMC) algorithms, perform poor

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

97 Following this, the Markov Chain Monte Carlo (MCMC) approach is applied to f

98 In this article, a new Markov chain Monte Carlo (MCMC) approach that solves bot

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 roviding a valuable addition to our previous Markov chain Monte Carlo (MCMC) approach.

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

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

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

106 as possible, we compared, within a Bayesian Markov Chain Monte Carlo (MCMC) framework, estimates of

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

108 A key element to a successful Markov chain Monte Carlo (MCMC) inference is the program

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

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

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

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

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

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

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

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

117 etworks and provide the first application of Markov chain Monte Carlo (MCMC) methods to experimental

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 Both inference methods use the same Markov chain Monte Carlo algorithm and differ from each

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

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

140 Our Markov chain Monte Carlo algorithm represents a general

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

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

143 We propose a new, fast Markov chain Monte Carlo algorithm to explore the poster

144 I develop a well-mixing Markov chain Monte Carlo algorithm to fit the models in

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

161 Bayesian Markov chain Monte Carlo analysis suggests a mean recent

162 Markov chain Monte Carlo and Monte Carlo sampling are us

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

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

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

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

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

168 We present a Markov chain Monte Carlo coalescent genealogy sampler, L

169 sian model/variable selection approach using Markov Chain Monte Carlo computations was applied to the

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

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

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

173 d a Bayesian version of our likelihood-based Markov chain Monte Carlo genealogy sampler LAMARC and co

174 A Markov chain Monte Carlo implementation based on the Gib

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

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

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

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

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

180 A Markov chain Monte Carlo mathematical approach can deter

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

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

183 ercome these limitations, we developed a new Markov chain Monte Carlo method to estimate parameters o

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

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

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

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

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

189 neous segregation and linkage analyses using Markov Chain Monte Carlo methods and detected linkage on

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

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

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

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

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

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

196 Markov chain Monte Carlo methods were used to estimate t

197 Markov chain Monte Carlo methods were used to sample key

198 ally solvable, several approaches, including Markov chain Monte Carlo methods, have been developed to

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

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

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

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

203 f coalescent genealogies was estimated using Markov chain Monte Carlo methods.

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

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

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

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

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

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

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

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

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

213 obtained via the posterior mean drawn from a Markov chain Monte Carlo sample.

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

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

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

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

218 A protocol using loki, a Markov chain Monte Carlo sampling method, was developed

219 Markov chain Monte Carlo sampling methods often suffer f

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

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

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

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

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

225 nce on the cross-experiment covariance using Markov chain Monte Carlo simulation to obtain an expecta

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

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

228 io) or linear (OMELOS) regression model with Markov Chain Monte Carlo simulation.

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

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

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

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

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

234 Application of a coalescent-based Markov chain Monte Carlo technique allows simultaneous i

235 A Markov chain Monte Carlo technique was developed to solv

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

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

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

239 is a Bayesian posterior sampler that employs Markov chain Monte Carlo to explore the joint space of a

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

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

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

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

244 ayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that

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 Model parameters are inferred through 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 Bayesian Markov chains Monte Carlo and associated time to most re

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

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

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

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

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

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

258 Bayesian techniques with the help of modern Markov-Chain Monte-Carlo methodology.

259 odology for joint mapping of QTLs, using the Markov chain-Monte Carlo (MCMC) algorithm.

260 Markov chain-Monte Carlo (MCMC)-based methods currently

261 A Markov chain-Monte Carlo scheme is designed to draw from

262 A Markov chain-Monte Carlo scheme is developed that yields

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

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

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

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

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

268 o, adaptive walks can be modeled as a simple Markov chain on the space of possible fitness ranks with

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

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

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

272 The Markov chain prediction model was used for the analysis

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

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

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

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

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

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

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

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

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

282 nformation from rapidly equilibrating coarse Markov chains that sample marginal distributions of the

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

284 We used Markov chain theory and maximum likelihood estimation to

285 Using Markov chain theory, we obtain analytical expressions fo

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

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

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

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

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

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

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

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