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1 inferring parameters of a partially observed 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 genomic ancestry inference as a pooled semi-Markov process.
6 occurs in the study of large deviations for Markov processes.
7 the associated discrete-time discrete-space Markov processes.
8 d in statistical mechanics and the theory of Markov processes.
9 cule time-binned FRET trajectories as hidden Markov processes, allowing one to determine, based on pr
10 s within blocks follows a time-inhomogeneous Markov process along the chromosome, and we choose among
11 sociation in a candidate region via a hidden Markov process and allow for correlation between linked
12 esent organelle inheritance as a first-order Markov process and describe two figures of merit based o
14 volution as a discrete space continuous time Markov process and introduce a neighbor-dependent model
15 on the description of protein evolution by a Markov process and the corresponding matrix of instantan
16 h is applicable to any system described by a Markov process and, owing to the analytic nature of the
17 neral mathematical framework for pooled semi-Markov processes and construct efficient inference algor
19 the gene tree and treats the coalescent as a Markov process describing the decay in the number of anc
20 We formulate a bivariate continuous-time Markov process for the numbers of T cells belonging to t
21 the channel is well described as a two-state Markov process, in which both the on- and off-rates are
24 opose multipoint methods that are based on a Markov-process model of allele sharing along the chromos
25 to perform likelihood calculations based on Markov process models of nucleotide substitution allied
27 coarse graining that reduces the model to a Markov process on a finite number of "information states
29 ection [1], [2], in which the evolution of a Markov process on the graph is used as a zooming lens ov
30 We constructed a decision analysis using the Markov process to model expected clinical outcomes and c
31 scillations, and it is demonstrated that for Markov processes to have oscillatory transients, its tra
32 ing the dynamics between helical states as a Markov process using a recently developed formalism.
33 tances are estimated by constructing spatial Markov processes using the information from both approxi
35 The model is formulated as a continuous time Markov process, which is decomposed into a deterministic
36 show that the probability p(t) that a Gauss-Markov process will first exceed the boundary at time t
37 titution model based on a general reversible Markov process with a gamma distribution to account for
38 lution for each of the eight categories is a Markov process with discrete states in continuous time,
40 l the community's confidence in a claim as a Markov process with successive published results shiftin
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