Results 111 to 120 of about 21,998,513 (385)
Advanced Science, EarlyView.DPImpute is a two‐step pipeline that outperforms existing tools in whole‐genome SNP imputation, particularly under conditions of ultra‐low coverage sequencing, small sample sizes, and limited references. It enables precise imputation for single blastocyst cells, supporting genomic selection at the pre‐implantation stage.
Weigang Zheng +11 more
wiley +1 more source
Estimation in autoregressive models with Markov regime [PDF]
arXiv, 2005 In this paper we derive the consistency of the penalized likelihood method for the number state of the hidden Markov chain in autoregressive models with Markov regimen. Using a SAEM type algorithm to estimate the models parameters. We test the null hypothesis of hidden Markov Model against an autoregressive process with Markov regime.
arxiv
On structural parameter estimation of the Markov Q-process [PDF]
Bull. Inst. Math., 2022, Vol.5, No1, pp. 44-55. [In Russian], 2022 In the paper we consider a stochastic model which called Markov Q-processes that forms a continuous-time Markov population system. Markov Q-processes are defined as stochastic Markov branching processes with trajectories continuing in the remote future. Estimation of the structural parameter of the Markov Q-process is the main goal of this paper.
arxiv
Hidden Markov Model Identifiability via Tensors
, 2013 The prevalence of hidden Markov models (HMMs) in various applications of statistical signal processing and communications is a testament to the power and flexibility of the model.
Nguyen, Hung X. +2 more
core +1 more source
Advanced Science, EarlyView.ProteoSeeker is a new metagenomic analysis tool that accelerates protein discovery. By automating key steps, it reduces computational complexity and enables comprehensive dataset analysis, performing protein identification, screening based on user‐defined protein families, and taxonomic analysis.
Georgios Filis +6 more
wiley +1 more source
Reflections on Bayesian inference and Markov chain Monte Carlo
Canadian Journal of Statistics, Volume 50, Issue 4, Page 1213-1227, December 2022., 2022 Abstract Bayesian inference and Markov chain Monte Carlo methods are vigorous areas of statistical research. Here we reflect on some recent developments and future directions in these fields. Résumé L'inférence bayésienne et les méthodes de Monte‐Carlo par chaîne de Markov sont des domaines dynamiques de la recherche statistique.
Radu V. Craiu +2 more
wiley +1 more source
Churn Prediction in Iran Banking Industry Case of a Private Iranian Bank [PDF]
پژوهشهای مدیریت عمومی, 2018 After the emergence of private banks in Iran, due to its attractiveness, this industry has witnessed a rapid growth such banks. The abundance of private banks led to a very high pressure competitive environment and gave dissatisfied customers a chance to
Mohsen Asgari +2 more
doaj +1 more source
Bioinform., 1998 The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous ...
S. Eddy
semanticscholar +1 more source
On sampling graphical Markov models [PDF]
arXiv, 2017 We consider sampling and enumeration problems for Markov equivalence classes. We create and analyze a Markov chain for uniform random sampling on the DAGs inside a Markov equivalence class. Though the worst case is exponentially slow mixing, we find a condition on the Markov equivalence class for polynomial time mixing. We also investigate the ratio of
arxiv
Entropy rate calculations of algebraic measures
, 2011 Let $K = \{0,1,...,q-1\}$. We use a special class of translation invariant measures on $K^\mathbb{Z}$ called algebraic measures to study the entropy rate of a hidden Markov processes.
Marchand, Katy +2 more
core +1 more source