Search results for "Bayesian probability"
showing 10 items of 217 documents
A New Simple Computational Method of Simultaneous Constructing and Comparing Confidence Intervals of Shortest Length and Equal Tails for Making Effic…
2021
A confidence interval is a range of values that provides the user with useful information about how accurately a statistic estimates a parameter. In the present paper, a new simple computational method is proposed for simultaneous constructing and comparing confidence intervals of shortest length and equal tails in order to make efficient decisions under parametric uncertainty. This unified computational method provides intervals in several situations that previously required separate analysis using more advanced methods and tables for numerical solutions. In contrast to the Bayesian approach, the proposed approach does not depend on the choice of priors and is a novelty in the theory of st…
A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.
2020
Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …
Additive noise and multiplicative bias as disclosure limitation techniques for continuous microdata: A simulation study
2004
This paper focuses on a combination of two disclosure limitation techniques, additive noise and multiplicative bias, and studies their efficacy in protecting confidentiality of continuous microdata. A Bayesian intruder model is extensively simulated in order to assess the performance of these disclosure limitation techniques as a function of key parameters like the variability amongst profiles in the original data, the amount of users prior information, the amount of bias and noise introduced in the data. The results of the simulation offer insight into the degree of vulnerability of data on continuous random variables and suggests some guidelines for effective protection measures.
Bayesian metanetworks for modelling user preferences in mobile environment
2003
The problem of profiling and filtering is important particularly for mobile information systems where wireless network traffic and mobile terminal’s size are limited comparing to the Internet access from the PC. Dealing with uncertainty in this area is crucial and many researchers apply various probabilistic models. The main challenge of this paper is the multilevel probabilistic model (the Bayesian Metanetwork), which is an extension of traditional Bayesian networks. The extra level(s) in the Metanetwork is used to select the appropriate substructure from the basic network level based on contextual features from user’s profile (e.g. user’s location). Two models of the Metanetwork are consi…
Estimation and visualization of confusability matrices from adaptive measurement data
2010
Abstract We present a simple but effective method based on Luce’s choice axiom [Luce, R.D. (1959). Individual choice behavior: A theoretical analysis. New York: John Wiley & Sons] for consistent estimation of the pairwise confusabilities of items in a multiple-choice recognition task with arbitrarily chosen choice-sets. The method combines the exact (non-asymptotic) Bayesian way of assessing uncertainty with the unbiasedness emphasized in the classical frequentist approach. We apply the method to data collected using an adaptive computer game designed for prevention of reading disability. A player’s estimated confusability of phonemes (or more accurately, phoneme–grapheme connections) and l…
A Bayesian-optimal principle for learner-friendly adaptation in learning games
2010
Abstract Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predic…
A Comparative Study to Analyze the Performance of Advanced Pattern Recognition Algorithms for Multi-Class Classification
2021
This study aims to implement the following four advanced pattern recognition algorithms, such as “optimal Bayesian classifier,” “anti-Bayesian classifier,” “decision trees (DTs),” and “dependence trees (DepTs)” on both artificial and real datasets for multi-class classification. Then, we calculated the performance of individual algorithms on both real and artificial data for comparison. In Sect. 1, a brief introduction is given about the study. In the second section, the different types of datasets used in this study are discussed. In the third section, we compared the classification accuracies of Bayesian and anti-Bayesian methods for both the artificial and real-life datasets. In the four…
Applications and Limitations of Robust Bayesian Bounds and Type II MLE
1994
Three applications of robust Bayesian analysis and three examples of its limitations are given. The applications that are reviewed are the development of an automatic Ockham’s Razor, outlier detection, and analysis of weighted distributions. Limitations of robust Bayesian bounds are highlighted through examples that include analysis of a paranormal experiment and a hierarchical model. This last example shows a disturbing difference between actual hierarchical Bayesian analysis and robust Bayesian bounds, a difference which also arises if, instead, a Type II MLE or empirical Bayes analysis is performed.
Bayesian Metanetwork for Context-Sensitive Feature Relevance
2006
Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of appropriate conditional dependency. However, depending on task and context, many attributes of the model might not be relevant. If a network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on a “relevance” of the predictive attributes towards tar…
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
2016
In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of info…