Nonparametric Bayes Methods Using Predictive Updating
نویسندگان
چکیده
Approximate nonparametric Bayes estimates calculated under a Dirichlet process prior are readily obtained in a wide range of models using a simple recursive algorithm. This chapter develops the recursion using elementary facts about nonparametric predictive distributions, and applies it to an interval censoring problem and to a Markov chain mixture model. S-Plus code is provided.
منابع مشابه
A nonparametric empirical Bayes framework for large-scale multiple testing.
We propose a flexible and identifiable version of the 2-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the nonnull cases. We use a computationally efficient predictive recursion (PR) marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonp...
متن کاملBayesian Nonparametric and Parametric Inference
This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.
متن کاملDetecting Differentially Expressed Genes Using Calibrated Bayes Factors
A common interest in microarray data analysis is to identify genes having changes in expression values between different biological conditions. The existing methods include using two-sample t-statistics, modified t-statistics (SAM), Bayesian t-statistics (Cyber-T), semiparametric hierarchical Bayesian models, and nonparametric permutation tests. All these methods essentially compare two populat...
متن کاملEvaluation of potential novel variations and their interactions related to bipolar disorders: analysis of genome-wide association study data
BACKGROUND Multifactor dimensionality reduction (MDR) is a nonparametric approach that can be used to detect relevant interactions between single-nucleotide polymorphisms (SNPs). The aim of this study was to build the best genomic model based on SNP associations and to identify candidate polymorphisms that are the underlying molecular basis of the bipolar disorders. METHODS This study was per...
متن کاملPredictive minimum Bayes risk classification for robust speech recognition
This paper presents a new Bayes classification rule towards minimizing the predictive Bayes risk for robust speech recognition. Conventionally, the plug-in maximum a posteriori (MAP) classification is constructed by adopting nonparametric loss function and deterministic model parameters. Speech recognition performance is limited due to the environmental mismatch and the ill-posed model. Concern...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998