scholarly journals Global Information for Multidimensional Tests

2021 ◽  
pp. 014662162110428
Author(s):  
Katherine G. Jonas
Keyword(s):  
Prior Distribution ◽  
Entire Range ◽  
Test Development ◽  
Simulated Data ◽  
Global Information ◽  
Global Test ◽  
Test Information ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Multidimensional Tests

New measures of test information, termed global information, quantify test information relative to the entire range of the trait being assessed. Estimating global information relative to a non-informative prior distribution results in a measure of how much information could be gained by administering the test to an unspecified examinee. Currently, such measures have been developed only for unidimensional tests. This study introduces measures of multidimensional global test information and validates them in simulated data. Then, the utility of global test information is tested in neuropsychological data collected as part of Rush University’s Memory and Aging Project. These measures allow for direct comparison of complex tests calibrated in different samples, facilitating test development and selection.

scholarly journals Global Information for Multidimensional Tests

2019 ◽  
Author(s):  
Katherine Jonas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Test Development ◽  
Global Information ◽  
Global Test ◽  
Test Information ◽  
Multidimensional Tests

New measures of test information quantify information that could be gained by administering the test to an examinee of unknown ability—termed global test information. Currently, these measures have been developed only for unidimensional tests. This study develops measures of multidimensional global information, validates them in data are simulated from models of varying dimensionality, and applies them in neuropsychological data from Rush University's Memory and Aging Project to identify the most informative tests of Alzheimer’s disease. These measures allow for direct comparison of complex tests normed in different samples, facilitating test development and selection.

A Characterization of the Exponential Distribution

1994 ◽  
Vol 44 (1-2) ◽  
pp. 123-126
Author(s):  
E. S. Jebvanand ◽  
N. Unnikrishnan Nair
Keyword(s):  
Exponential Distribution ◽  
Prior Distribution ◽  
Future Observation ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Continuous Population

In this note we prove that the exponential distribution is characterized by the property [Formula: see text] where Y is a future observation and x1, x2,…, x n are identical and independently distributed observations from a continuous population with density f( x; a), where a is assumed to have a non-informative prior distribution

scholarly journals Bayesian Estimation of the Limiting Availability in a Repairable One-Unit System

2019 ◽  
Vol 42 (1) ◽  
pp. 223-143
Author(s):  
Víctor H. Salinas Torres ◽  
Cristián A. Vásquez ◽  
José S. Romeo
Keyword(s):  
Prior Distribution ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Coherent System ◽  
Repairable System ◽  
Unit System ◽  
System A ◽  
Informative Prior ◽  
Informative Prior Distribution

 This work presents a Bayesian approach for estimating the limiting availability of an one-unit repairable system. A Bayesian analysis is developed considering an informative prior and a less informative prior distribution, respectively. Simulations are presented to study the performance of the Bayesian solutions. The maximum likelihood method is also revisited. Finally, a case study is considered, the Bayesian methodology is applied to estimate the limiting availability of a palletizer, which is used in the packaging of glass bottles. Extensions to a coherent system are also discussed.

scholarly journals Updating Knowledge in The Estimation of The Genetics Parameters Multi-trait and Multi-Environment Bayesian Analysis in Rice (Oryza Sativa L.)

2021 ◽  
Author(s):  
Camila Ferreira Azevedo ◽  
Cynthia Barreto ◽  
Matheus Suela ◽  
Moysés Nascimento ◽  
Antônio Carlos Júnior ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Prior Distribution ◽  
Variance Components ◽  
Genetic Correlations ◽  
Bayesian Framework ◽  
Prior Distributions ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Informative Prior Distributions ◽  
The Bayesian Approach

Abstract Among the multi-trait models used to jointly study several traits and environments, the Bayesian framework has been a preferable tool for using a more complex and biologically realistic model. In most cases, the non-informative prior distributions are adopted in studies using the Bayesian approach. Still, the Bayesian approach tends to present more accurate estimates when it uses informative prior distributions. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models under a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data from rice. The study involved data pertained to rice genotypes in three environments and five agricultural years (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components and genetic and non-genetic parameters were estimated by the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. The use of more informative prior distributions makes it possible to detect genetic correlations between traits, which cannot be achieved with the use of non-informative prior distributions. Therefore, this mechanism presented for updating knowledge to the elicitation of an informative prior distribution can be efficiently applied in rice genetic selection.

scholarly journals A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models

2021 ◽  
Vol 12 ◽  
Author(s):  
Christoph König
Keyword(s):  
Multiple Regression ◽  
Similarity Measure ◽  
Prior Distribution ◽  
Regression Models ◽  
Prior Distributions ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Multiple Regression Models ◽  
Power Prior ◽  
Informative Prior Distributions

Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ωas a weight for informative prior distributions for the substantial parameters (regression coefficients) in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions.

The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses

2019 ◽  
pp. 61-72
Author(s):  
Therese M. Donovan ◽  
Ruth M. Mickey
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Inference ◽  
Prior Distribution ◽  
Alternative Hypothesis ◽  
A Priori ◽  
Prior Probabilities ◽  
Birthday Problem ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Alternative Hypotheses

The “Birthday Problem” expands consideration from two hypotheses to multiple, discrete hypotheses. In this chapter, interest is in determining the posterior probability that a woman named Mary was born in a given month; there are twelve alternative hypotheses. Furthermore, consideration is given to assigning prior probabilities. The priors represent a priori probabilities that each alternative hypothesis is correct, where a priori means “prior to data collection,” and can be “informative” or “non-informative.” A Bayesian analysis cannot be conducted without using a prior distribution, whether that is an informative prior distribution or a non-informative prior distribution. The chapter discusses objective priors, subjective priors, and prior sensitivity analysis. In addition, the concept of likelihood is explored more deeply.

scholarly journals Constrained Adjusted Maximum a Posteriori Estimation of Bayesian Network Parameters

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1283
Author(s):  
Ruohai Di ◽  
Peng Wang ◽  
Chuchao He ◽  
Zhigao Guo
Keyword(s):  
Prior Distribution ◽  
Domain Knowledge ◽  
Maximum A Posteriori ◽  
Parameter Distribution ◽  
A Posteriori ◽  
Maximum A Posteriori Estimation ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Parameter Constraints ◽  
Posteriori Estimation

Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often considered for regularization. However, when the underlying parameter distribution is non-uniform or skewed, uniform prior does not work well, and a more informative prior is required. In reality, unless the domain experts are extremely unfamiliar with the network, they would be able to provide some reliable knowledge on the studied network. With that knowledge, we can automatically refine informative priors and select reasonable equivalent sample size (ESS). In this paper, considering the parameter constraints that are transformed from the domain knowledge, we propose a Constrained adjusted Maximum a Posteriori (CaMAP) estimation method, which is featured by two novel techniques. First, to draw an informative prior distribution (or prior shape), we present a novel sampling method that can construct the prior distribution from the constraints. Then, to find the optimal ESS (or prior strength), we derive constraints on the ESS from the parameter constraints and select the optimal ESS by cross-validation. Numerical experiments show that the proposed method is superior to other learning algorithms.

Evaluating Content-Management Systems for Online Learning Programs

2006 ◽  
pp. 292-305
Author(s):  
Deborah L. Schnipke ◽  
Kirk Becker ◽  
James S. Masters
Keyword(s):  
Online Learning ◽  
Test Development ◽  
Content Management ◽  
Management Systems ◽  
Test Information ◽  
Item Banks ◽  
Learning Programs ◽  
Quality Assessments ◽  
Different Content ◽  
Content Management Systems

Creating quality assessments typically requires the involvement of many people who require access to the item and test information, which is stored in repositories called item banks or, more appropriately, content-management systems, since they store many kinds of content used in the test development process. This chapter discusses the types of options that are available in content-management systems and provides guidance about how to evaluate whether different content-management systems will meet an organization’s test development and delivery needs. This chapter focuses on online, fully Internet-enabled applications, since those applications have the most features.

Sign in / Sign up

Export Citation Format

Share Document