Principles and Methods for Data Science
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Principles and Methods for Data Science
Rao, C.R.; Srinivasa Rao, Arni S.R.
Elsevier Science & Technology
05/2020
496
Dura
Inglês
9780444642110
15 a 20 dias
980
Descrição não disponível.
Markov chain Monte Carlo methods: Theory and practice
David A. Spade
An information and statistical analysis pipeline for microbial metagenomic sequencing data
Shinji Nakaoka and Keisuke Ohta
Machine learning algorithms, applications, and practices in data science
Kalidas Yeturu
Bayesian model selection for high-dimensional data
Naveen Naidu Narisetty
Competing risks: Aims and methods
Ronald Geskus
High-dimensional statistical inference: Theoretical development to data analytics
Deepak Nag Ayyala
Big data challenges in genomics
Hongyan Xu
Analysis of microarray gene expression data using information theory and stochastic algorithm
Narayan Behera
Human life expectancy is computed from an incomplete sets of data: Modeling and analysis
Arni S.R. Srinivasa Rao and James R. Carey
Support vector machines: A robust prediction method with applications in bioinformatics
Arnout Van Messem
David A. Spade
An information and statistical analysis pipeline for microbial metagenomic sequencing data
Shinji Nakaoka and Keisuke Ohta
Machine learning algorithms, applications, and practices in data science
Kalidas Yeturu
Bayesian model selection for high-dimensional data
Naveen Naidu Narisetty
Competing risks: Aims and methods
Ronald Geskus
High-dimensional statistical inference: Theoretical development to data analytics
Deepak Nag Ayyala
Big data challenges in genomics
Hongyan Xu
Analysis of microarray gene expression data using information theory and stochastic algorithm
Narayan Behera
Human life expectancy is computed from an incomplete sets of data: Modeling and analysis
Arni S.R. Srinivasa Rao and James R. Carey
Support vector machines: A robust prediction method with applications in bioinformatics
Arnout Van Messem
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Aalen-Johansen estimator; Artificial intelligence; Asymptotics; Automatic differentiation; Bayesian computation; Bayesian statistics; Bayesian variable selection; Bioinformatics; Boosting; Cancer-causing genes; Classification; Classification accuracy; Clustering; Computational biology; Convergence; Data science; Data volume; Deep learning; Dependent data; Disease genomics; Explanation vs prediction; Genetic algorithm; Genome microbiology; Gradient descent; Graphical models; High-dimensional data; High-dimensional inference; History of life expectancy; Human genomics; Hypothesis testing; Influence function; Information analysis pipeline; Lyapunov conditions; Machine learning; Metropolis-Hastings; Microarray gene expression data; Mixing time; Model comparison; Modeling; Multivariate analysis; Mutual information; Next-generation sequencing; Nonparametric estimation; Parametric; Population biology; Product-limit estimator; Regression; RNA-Seq; Robustness; Shotgun metagenomics; Subdistribution; Supervised models; Support vector machines; SVD; Time-varying covariable
Markov chain Monte Carlo methods: Theory and practice
David A. Spade
An information and statistical analysis pipeline for microbial metagenomic sequencing data
Shinji Nakaoka and Keisuke Ohta
Machine learning algorithms, applications, and practices in data science
Kalidas Yeturu
Bayesian model selection for high-dimensional data
Naveen Naidu Narisetty
Competing risks: Aims and methods
Ronald Geskus
High-dimensional statistical inference: Theoretical development to data analytics
Deepak Nag Ayyala
Big data challenges in genomics
Hongyan Xu
Analysis of microarray gene expression data using information theory and stochastic algorithm
Narayan Behera
Human life expectancy is computed from an incomplete sets of data: Modeling and analysis
Arni S.R. Srinivasa Rao and James R. Carey
Support vector machines: A robust prediction method with applications in bioinformatics
Arnout Van Messem
David A. Spade
An information and statistical analysis pipeline for microbial metagenomic sequencing data
Shinji Nakaoka and Keisuke Ohta
Machine learning algorithms, applications, and practices in data science
Kalidas Yeturu
Bayesian model selection for high-dimensional data
Naveen Naidu Narisetty
Competing risks: Aims and methods
Ronald Geskus
High-dimensional statistical inference: Theoretical development to data analytics
Deepak Nag Ayyala
Big data challenges in genomics
Hongyan Xu
Analysis of microarray gene expression data using information theory and stochastic algorithm
Narayan Behera
Human life expectancy is computed from an incomplete sets of data: Modeling and analysis
Arni S.R. Srinivasa Rao and James R. Carey
Support vector machines: A robust prediction method with applications in bioinformatics
Arnout Van Messem
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Aalen-Johansen estimator; Artificial intelligence; Asymptotics; Automatic differentiation; Bayesian computation; Bayesian statistics; Bayesian variable selection; Bioinformatics; Boosting; Cancer-causing genes; Classification; Classification accuracy; Clustering; Computational biology; Convergence; Data science; Data volume; Deep learning; Dependent data; Disease genomics; Explanation vs prediction; Genetic algorithm; Genome microbiology; Gradient descent; Graphical models; High-dimensional data; High-dimensional inference; History of life expectancy; Human genomics; Hypothesis testing; Influence function; Information analysis pipeline; Lyapunov conditions; Machine learning; Metropolis-Hastings; Microarray gene expression data; Mixing time; Model comparison; Modeling; Multivariate analysis; Mutual information; Next-generation sequencing; Nonparametric estimation; Parametric; Population biology; Product-limit estimator; Regression; RNA-Seq; Robustness; Shotgun metagenomics; Subdistribution; Supervised models; Support vector machines; SVD; Time-varying covariable
