ライフサイエンスコーパス: Markov process

1 genomic ancestry inference as a pooled semi-Markov process.

2 ty that reflects the strength of mixing in a Markov process.

3 nce) was analyzed with a computer model of a Markov process.

4 substitution is modeled by a continuous-time Markov process.

5 imilar to those of SCORE2 participants and a Markov process.

6 m with an exact and efficient non-reversible Markov process.

7 inferring parameters of a partially observed Markov process.

8 occurs in the study of large deviations for Markov processes.

9 the associated discrete-time discrete-space Markov processes.

10 the single-step dependency characteristic of Markov processes.

11 d in statistical mechanics and the theory of Markov processes.

12 cule time-binned FRET trajectories as hidden Markov processes, allowing one to determine, based on pr

13 s within blocks follows a time-inhomogeneous Markov process along the chromosome, and we choose among

14 sociation in a candidate region via a hidden Markov process and allow for correlation between linked

15 esent organelle inheritance as a first-order Markov process and describe two figures of merit based o

16 We quantify the phenomenon as a Markov process and discover that if the network fails to

17 Our model of pooled semi-Markov process and inference algorithms may be of indepe

18 volution as a discrete space continuous time Markov process and introduce a neighbor-dependent model

19 on the description of protein evolution by a Markov process and the corresponding matrix of instantan

20 h is applicable to any system described by a Markov process and, owing to the analytic nature of the

21 neral mathematical framework for pooled semi-Markov processes and construct efficient inference algor

22 hod accurately infers parameters of the semi-Markov processes and parents' genomic ancestries.

23 the harmonic fields can be induced by simple Markov processes and that the corresponding stochastic d

24 P models latent phenotype states as a binary Markov process, and it employs an adaptive weighting str

25 linear framework, a graph-based approach to Markov processes, and show that it can accommodate many

26 multistate life tables under a discrete-time Markov process assumption.

27 ons: First, we show how it can be applied to Markov processes biased by arbitrary reweighting factors

28 g the memoryless property, consistent with a Markov process, but it overestimates the probability of

29 the gene tree and treats the coalescent as a Markov process describing the decay in the number of anc

30 We formulate a bivariate continuous-time Markov process for the numbers of T cells belonging to t

31 the channel is well described as a two-state Markov process, in which both the on- and off-rates are

32 In the algorithm a continuous Markov process is discretized as a jump process and the

33 Tools from the theory of non-Markov processes may help us understand these memories b

34 of any input-output response arising from a Markov process model at thermodynamic equilibrium.

35 A Markov process model is used for nucleotide substitution

36 opose multipoint methods that are based on a Markov-process model of allele sharing along the chromos

37 to perform likelihood calculations based on Markov process models of nucleotide substitution allied

38 In most cases, a Markov process of at least fourth-order was required to

39 lly relevant examples that at least for semi-Markov processes of first and second order, waiting-time

40 coarse graining that reduces the model to a Markov process on a finite number of "information states

41 The underlying model takes the form of a Markov process on an infinite dimensional state space.

42 ection [1], [2], in which the evolution of a Markov process on the graph is used as a zooming lens ov

43 We develop a theory of rattling in terms of Markov processes that gives simple and precise answers t

44 apshot time series data as a low-dimensional Markov process, thereby enabling an interpretable dynami

45 We constructed a decision analysis using the Markov process to model expected clinical outcomes and c

46 scillations, and it is demonstrated that for Markov processes to have oscillatory transients, its tra

47 fluorophore as an unobserved continuous time Markov process transitioning between a single fluorescen

48 ing the dynamics between helical states as a Markov process using a recently developed formalism.

49 tances are estimated by constructing spatial Markov processes using the information from both approxi

50 A Markov process was constructed to project the natural hi

51 Using the theory of linear elasticity and Markov processes, we simulate a model, which reproduces

52 how that 3D cancer cell motility is a hidden Markov process whereby the step size distributions of ce

53 The model is formulated as a continuous time Markov process, which is decomposed into a deterministic

54 show that the probability p(t) that a Gauss-Markov process will first exceed the boundary at time t

55 titution model based on a general reversible Markov process with a gamma distribution to account for

56 lution for each of the eight categories is a Markov process with discrete states in continuous time,

57 n described as an aggregated continuous-time Markov process with discrete states.

58 l the community's confidence in a claim as a Markov process with successive published results shiftin

59 ented that use the theory of continuous-time Markov processes with discontinuous sample paths.