Results 1 to 10 of about 152,289 (138)
Fast parallel construction of variable-length Markov chains [PDF]
BMC Bioinformatics, 2021 Background Alignment-free methods are a popular approach for comparing biological sequences, including complete genomes. The methods range from probability distributions of sequence composition to first and higher-order Markov chains, where a k-th order ...
Joel Gustafsson +3 more
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Ancestry inference in complex admixtures via variable-length Markov chain linkage models. [PDF]
J Comput Biol, 2013 Inferring the ancestral origin of chromosomal segments in admixed individuals is key for genetic applications, ranging from analyzing population demographics and history, to mapping disease genes. Previous methods addressed ancestry inference by using either weak models of linkage disequilibrium, or large models that make explicit use of ancestral ...
Rodriguez JM +3 more
europepmc +4 more sources
On the Structure of the World Economy: An Absorbing Markov Chain Approach
Entropy, 2020 The expansion of global production networks has raised many important questions about the interdependence among countries and how future changes in the world economy are likely to affect the countries’ positioning in global value chains.
Olivera Kostoska +2 more
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Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2006 Markov models have been widely used to represent and analyse user web navigation data. In previous work we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of ...
Borges, Jose, Levene, Mark
core +2 more sources
A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses. [PDF]
J Comput Biol, 2022 The statistical inference of high-order Markov chains (MCs) for biological sequences is vital for molecular sequence analyses but can be hindered by the high dimensionality of free parameters. In the seminal article by Bühlmann and Wyner, variable length Markov chain (VLMC) model was proposed to embed the full-order MC in a sparse structured context ...
An S, Ren J, Sun F, Wan L.
europepmc +3 more sources
Research on online EDI order scheduling optimization strategy in manufacturing enterprises based on time-varying Markov chains [PDF]
Scientific ReportsIn the era of Industry 5.0 and artificial intelligence, with the continuous development of online EDI orders, manufacturing enterprises adopting a combined online and offline order acceptance model face the challenge of optimizing production scheduling ...
Qiqige Wulan
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Variable length Markov chains: methodology, computing and software [PDF]
Annals of the Institute of Statistical Mathematics, 2002 We present a tutorial and new publicly available computational tools for variable length Markov chains (vlmc). vlmc’s are Markov chains with the additional at tractive structure that their memories depend on a variable number of lagged values, depending on how the actual past (the lagged values) looks like.
Mächler, Martin; id_orcid0000-0002-8685-9910 +1 more
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Variable length Markov chain with exogenous covariates
Journal of Time Series Analysis, 2021 Markov chains with variable length are useful stochastic models for data compression that avoid the curse of dimensionality faced by full Markov chains. In this article we introduce a variable length Markov chain whose transition probabilities depend not only on the state history but also on exogenous covariates through a generalized linear model.
Adriano Zanin Zambom +2 more
openaire +2 more sources
Nonreversible Markov Chain Monte Carlo Algorithm for Efficient Generation of Self-Avoiding Walks
Frontiers in Physics, 2022 We introduce an efficient nonreversible Markov chain Monte Carlo algorithm to generate self-avoiding walks with a variable endpoint. In two dimensions, the new algorithm slightly outperforms the two-move nonreversible Berretti-Sokal algorithm introduced ...
Hanqing Zhao, Marija Vucelja
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ToPS: a framework to manipulate probabilistic models of sequence data. [PDF]
PLoS Computational Biology, 2013 Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling.
André Yoshiaki Kashiwabara +5 more
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