Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers - Leonard, Hsu - 1999 () Citation Context ...lysis are potentially overcome by using what is known as the Laplacian approximation to the marginal probability (see, for example, Carlin & Louis, 2000, pp.122–129; ...
Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal. 展开▼ 机...
Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal. View article ...
Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal. 展开 关键词...
By contrast,Bayesian analysisprovides coherent methods for computing the power and replication probability. Bayesian power analysis uses the posterior distribution to sample many different plausible parameter values, and for each parameter value generates plausible data that simulate a repetition of the exper...
p pBackground/p pWith the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values...
Springer Texts in Statistics(共118册), 这套丛书还有 《Statistics and Data Analysis for Financial Engineering》《An Introduction to Statistical Learning》《Theory of Multivariate Statistics》《Monte Carlo Statistical Methods》《Statistical Methods for Quality Assurance: Basics, Measurement, Control, Capability...
As mentioned in the preface, the objective of this text is to familiarize graduate students with the basic concepts of Bayesian theory and to quickly get them performing data analysis using Bayesian computational tools. Explaining why Bayesian methods are necessary, the stage is set for introduction...
A First Course in Bayesian Statistical Methods 电子书 读后感 评分☆☆☆ 读这本书之前应该读一本《Statistical Inference》或者《Probability and Statistics》这样的书,否则会被那些beta函数、gamma函数搞晕。 不过这本书似乎更偏重于思想,而不是数学推导。 评分☆☆☆ 标题都说了,贝叶斯统计方法的第一堂课...
Kernel methods for deep learning. In Advances in Neural Information Processing Systems (eds Bengio, Y. et al.) Vol. 22 (Curran Associates, 2009). Poole, B., Lahiri, S., Raghu, M., Sohl-Dickstein, J. & Ganguli, S. Exponential expressivity in deep neural networks through transient chaos...