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Testing the statistical significance of an ultra-high-dimensional naïve Bayes classfier
Binary Predictor Hypothesis Testing Na?ve Bayes Supervised Learning
2016/1/25
The na?ve Bayes approach is one of the most popular methods used for classi?cation. Nevertheless, how to test its statistical signi?cance under an ultra-high-dimensional (UHD) setup is not well unders...
Varying Naive Bayes Models with Applications toClassi cation of Chinese Text Documents
BIC Chinese Document Classification Screening Consistency Time-dependent Classification Rule
2016/1/20
Document classification is an area of great importance for which many clas-sification methods have been well developed. However, most of these methods cannot generate time-dependent classification rul...
Testing the statistical significance of an ultra-high-dimensional naïve Bayes classfier
Binary Predictor Hypothesis Testing Na?ve Bayes Supervised Learning
2016/1/20
The na?ve Bayes approach is one of the most popular methods used for classi?cation. Nevertheless, how to test its statistical signi?cance under an ultra-high-dimensional(UHD) setup is not well underst...
Adaptive Bayes test for monotonicity
Bayesian Nonparametric Nonparametric regression Nonparamet-ric hypothesis testing Asymptotic properties
2013/4/28
We study the asymptotic behaviour of a Bayesian nonparametric test of qualitative hypotheses. More precisely, we focus on the problem of testing monotonicity of a regression function. Even if some res...
Refinement revisited with connections to Bayes error, conditional entropy and calibrated classifiers
Refinement Score Probability Elicitation Calibrated Classifier Bayes Error Bound Conditional Entropy Proper Loss
2013/4/27
The concept of refinement from probability elicitation is considered for proper scoring rules. Taking directions from the axioms of probability, refinement is further clarified using a Hilbert space i...
An Interacting Particle Method for Approximate Bayes Computations
Interacting Method Bayes Computations
2012/9/17
Approximate Bayes Computations (ABC) are used for parameter inference when the likelihood function is expensive to evaluate but relatively cheap to sample from. In ABC,a population of particles in the...
单变量Bayes正态动态线性模型及其预测
动态线性模型 时间序列 Bayes预测
2013/8/13
Bayes预测和动态模型是20世纪70年代发展起来的一套新的时间序列分析方法，其中单变量Bayes正态动态线性模型(UBNDLM)在实际应用中最为常见和重要。在UBNDLM应用中，通常假定观测误差方差是未知常量，其值在建模的开始由估计给出。一般的做法是对具体的问题通过专家经验给出，并没有一个统一有效的办法。文章针对这一情形，给出了观测误差方差值一种简单易用的估计方法。同时也提供了数值试验说明新方法...
A Variational Bayes Approach to Decoding in a Phase-Uncertain Digital Receiver
Variational Bayes approximation phase synchronization
2011/7/19
This paper presents a Bayesian approach to symbol and phase inference in a phase-unsynchronized digital receiver. It primarily extends [Quinn 2011] to the multi-symbol case, using the variational Baye...
A nonparametric empirical Bayes framework for large-scale multiple testing
Dirichlet process marginal likelihood mixture model
2011/7/6
We propose a flexible and identifiable version of the two-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-n...
Bounds on the Maximum Bayes Error Given Moments
the Maximum Bayes Error class-conditional Curto Fialkow’s solutions
2011/6/17
We show how to compute lower bounds for the maximum possible Bayes error if the class-conditional distributions
must satisfy moment constraints. Our approach makes use of Curto and Fialkow’s solution...
Variational Bayes approach for model aggregation in unsupervised classification with Markovian dependency
Model averaging Variational Bayes inference Markov Chain Unsu-pervised classification
2011/6/16
We consider a binary unsupervised classi cation problem where each observation
is associated with an unobserved label that we want to retrieve. More precisely, we
assume that there are two groups of...
Chi-square Intervals for a Poisson Parameter - Bayes, Classical and Structural
Confidence interval coverage probability estimation interval Poisson
2011/3/18
The 'standard' confidence interval for a Poisson parameter is only one of a number of estimation intervals based on the chi-square distribution that may be used in the estimation of the mean or mean r...
基于Bayes概率边界域的粗集分类方法及其在高频数据中的应用
可变精度粗糙集 Bayes边界域 高频数据
2013/8/23
作为一种近似处理的工具，粗集主要用于不确定情况下的决策分析，并且不需要任何事先的数据假定。但当前的主流粗集分类方法仍然需要先经过离散化的步骤，这就损失了数值型变量提供的高质量信息。本文对隶属函数重新加以概率定义，并提出了一种基于Bayes概率边界域的粗集分类技术，比较好地解决了当前粗集方法所面临的数值型属性分类的不适应、分类规则不完备等一系列问题。
An empirical Bayes mixture method for effect size and false discovery rate estimation
Empirical Bayes false discovery rate effect size estimation
2010/10/19
Many statistical problems involve data from thousands of parallel cases. Each case has some associated effect size, and most cases will have no effect. It is often important to estimate the effect siz...
The Bayes oracle and asymptotic optimality of multiple testing procedures under sparsity
Multiple testing FDR Bayes oracle asymptotic optimality
2010/3/10
We investigate the asymptotic optimality of a large class of multiple testing rules using the
framework of Bayesian Decision Theory. We consider a parametric setup, in which observations
come from a...