Results 41 to 50 of about 152,408 (254)

Variable Selection of Exogenous Covariates in Variable Length Markov Chains

Stat
ABSTRACTModelling time series data with complex dependencies poses significant challenges. While higher‐order Markov chains can capture longer memory of the process, they can suffer from the curse of dimensionality. Variable length Markov chains (VLMCs) offer a more efficient alternative by focusing on relevant past information. This paper introduces a
Adriano Zanin Zambom, Parsa Hashemi‐Alavi   +1 more
openaire   +1 more source

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

On the first k moments of the random count of a pattern in a multi-states sequence generated by a Markov source [PDF]

, 2009
In this paper, we develop an explicit formula allowing to compute the first k moments of the random count of a pattern in a multi-states sequence generated by a Markov source.
Nuel, Grégory
core   +3 more sources

Investigating Phase Separation in Genome Folding via Multiscale Computational Modeling

Advanced Science, EarlyView.
Phase separation is emerging as a multiscale organizing principle of genome folding across scales, from nucleosomes and transcriptional condensates to chromatin domains and nuclear compartments. By integrating physics‐based simulations with data‐driven inference, computational modeling now links molecular interactions to nuclear architecture and points
Jiahu Tang, Cibo Feng, Haibin Su, Xiakun Chu   +3 more
wiley   +1 more source

Dry spell length analysis for crop production using Markov-Chain model in Eastern Hararghe, Ethiopia

Journal of Degraded and Mining Lands Management, 2017
The information on the length of dry spells could be used for deciding a particular crop or variety, supplementary irrigation water demand and for others agricultural activities.
Eba Muluneh Sorecha, Birhanu Bayissa
doaj   +1 more source

Are Lock-Free Concurrent Algorithms Practically Wait-Free? [PDF]

, 2013
Lock-free concurrent algorithms guarantee that some concurrent operation will always make progress in a finite number of steps. Yet programmers prefer to treat concurrent code as if it were wait-free, guaranteeing that all operations always make progress.
Alistarh, Dan, Censor-Hillel, Keren, Shavit, Nir   +2 more
core   +3 more sources

Inference for Stochastically Contaminated Variable Length Markov Chains

, 2017
In this paper, we present a methodology to estimate the parameters of stochastically contaminated models under two contamination regimes. In both regimes, we assume that the original process is a variable length Markov chain that is contaminated by a random noise. In the first regime we consider that the random noise is added to the original source and
Duarte, Denise, Ndreca, Sokol, Prates, Wecsley O.   +2 more
openaire   +2 more sources

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

AIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi, Tomoyuki Yajima, Yoshiaki Kawajiri   +2 more
wiley   +1 more source

Dynamic Framing and Power Allocation for Real-Time Wireless Networks with Variable-Length Coding: A Tandem Queue Approach

Network
Ensuring high reliability and low latency poses challenges for numerous applications that require rigid performance guarantees, such as industrial automation and autonomous vehicles.
Yuanrui Liu, Xiaoyu Zhao, Wei Chen, Ying-Jun Angela Zhang   +3 more
doaj   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source