水平
发表于 2025-3-21 16:18:04
书目名称Nonparametric Bayesian Inference in Biostatistics影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0667816<br><br> <br><br>书目名称Nonparametric Bayesian Inference in Biostatistics读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0667816<br><br> <br><br>
Tempor
发表于 2025-3-21 22:10:18
Bayesian Nonparametric Biostatisticsntribute to addressing some fundamental questions that arise in biomedical research. In particular, we review some modern Bayesian semi- and nonparametric approaches for modeling longitudinal, survival, and medical diagnostic outcome data. Our discussion includes methods for longitudinal data analys
Flat-Feet
发表于 2025-3-22 00:54:58
Bayesian Shape Clusteringages. Existing model-based clustering techniques rely on simple probability models that are not generally valid for analyzing shapes of curves. In this chapter, we talk about an efficient Bayesian method to cluster curve data using a carefully chosen metric on the shape space. Rather than modeling t
dithiolethione
发表于 2025-3-22 05:09:09
Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Modelsd by mutations in sequence and structure at various genomic locations. Using next-generation sequencing data, we characterize tumor heterogeneity using Bayesian nonparametric inference. Specifically, we estimate the number of subclones in a tumor sample, and for each subclone, we estimate the subclo
STIT
发表于 2025-3-22 09:39:22
Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Abernparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, common exchangeability assumptions may not be appropriate. We discuss a generalization of species sampling sequences, where the weights in the predictive probability functions
PAC
发表于 2025-3-22 13:21:45
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Ordeal
发表于 2025-3-22 17:33:06
Bayesian Inference on Population Structure: From Parametric to Nonparametric Modeling. The source populations are assumed to be in Hardy-Weinberg equilibrium, but the allelic frequencies of these populations and even the number of populations present in a sample are unknown. In this chapter we present a review of some Bayesian parametric and nonparametric models for making inference
礼节
发表于 2025-3-22 21:41:54
Bayesian Approaches for Large Biological Networksression data. Methods that employ sparse priors have been particularly successful, as they are properly designed to analyze large datasets in which the amount of measured variables can be greater than the number of observations. Here, we describe Bayesian approaches for both undirected and directed
AVID
发表于 2025-3-23 02:09:25
Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets due to relatively small sample sizes and the presence of complex interaction patterns between a large number of covariates. We propose an efficient, nonparametric framework for simultaneous variable selection, clustering and prediction in high-throughput regression settings with continuous outcomes
monogamy
发表于 2025-3-23 06:23:26
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