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Identifying Transfer Learning in the Reshaping of Inductive Biases. [PDF]

Open Mind (Camb)
Székely A, Török B, Kiss M, Janacsek K, Németh D, Orbán G.   +5 more
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Bayesian phylodynamic inference of multi-type population trajectories using genomic data

Vaughan TG, Stadler T.
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Truncation Bounds for Approximations of Inhomogeneous Continuous-Time Markov Chains

Theory of Probability & Its Applications, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zeifman, A. I., Korotysheva, A. V., Korolev, V. Yu., Satin, Ya. A.   +3 more
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On the Numerical Analysis of Inhomogeneous Continuous-Time Markov Chains

INFORMS Journal on Computing, 2010
Inhomogeneous continuous-time Markov chains play an important role in different application areas. In contrast to homogeneous continuous-time Markov chains, where a large number of numerical analysis techniques are available and have been compared, few results about the performance of numerical techniques in the inhomogeneous case are known.
M. Arns, P. Buchholz, A. Panchenko
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On the Markov Property of the Occupation Time for Continuous-Time Inhomogeneous Markov Chains

Journal of Mathematical Sciences, 2015
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On the Study of Forward Kolmogorov System and the Corresponding Problems for Inhomogeneous Continuous-Time Markov Chains

2020
An inhomogeneous continuous-time Markov chain X(t) with finite or countable state space under some natural additional assumptions is considered. As a consequence, we study a number of problems for the corresponding forward Kolmogorov system, which is the linear system of differential equations with special structure of the matrix A(t). In the countable
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Analysis of panel data under continuous time-inhomogeneous Markov chains with stochastic covariates intensity

This paper revisits Kalbfleisch and Lawless (1985) and develops a novel approach for analysis of panel data under continuous time-inhomogeneous Markov chains whose intensity matrix depends on stochastic covariates. Unlike the mentioned paper, the new model assumes continuous observation of the Markov chains.
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