Prior Probability

2017 ◽  
pp. 1006-1006
Author(s):  
Geoffrey I. Webb
Keyword(s):  
Prior Probability

Encoding of graded changes in spatial specificity of prior cues in human visual cortex

2014 ◽  
Vol 112 (11) ◽  
pp. 2834-2849 ◽  
Author(s):  
Yuko Hara ◽  
Justin L. Gardner
Keyword(s):  
Prior Information ◽  
Prior Probability ◽  
Blood Oxygenation ◽  
Response Magnitude ◽  
Behavioral Performance ◽  
Significant Difference ◽  
Cortical Responses ◽  
Sensory Responses ◽  
Contrast Discrimination ◽  
Spatial Specificity

Prior information about the relevance of spatial locations can vary in specificity; a single location, a subset of locations, or all locations may be of potential importance. Using a contrast-discrimination task with four possible targets, we asked whether performance benefits are graded with the spatial specificity of a prior cue and whether we could quantitatively account for behavioral performance with cortical activity changes measured by blood oxygenation level-dependent (BOLD) imaging. Thus we changed the prior probability that each location contained the target from 100 to 50 to 25% by cueing in advance 1, 2, or 4 of the possible locations. We found that behavioral performance (discrimination thresholds) improved in a graded fashion with spatial specificity. However, concurrently measured cortical responses from retinotopically defined visual areas were not strictly graded; response magnitude decreased when all 4 locations were cued (25% prior probability) relative to the 100 and 50% prior probability conditions, but no significant difference in response magnitude was found between the 100 and 50% prior probability conditions for either cued or uncued locations. Also, although cueing locations increased responses relative to noncueing, this cue sensitivity was not graded with prior probability. Furthermore, contrast sensitivity of cortical responses, which could improve contrast discrimination performance, was not graded. Instead, an efficient-selection model showed that even if sensory responses do not strictly scale with prior probability, selection of sensory responses by weighting larger responses more can result in graded behavioral performance benefits with increasing spatial specificity of prior information.

Bayesian Networks Application in Multi-State System Reliability Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 2590-2595 ◽  
Author(s):  
Sheng Zhai ◽  
Shu Zhong Lin
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
System Reliability ◽  
Prior Probability ◽  
State System ◽  
Weak Links ◽  
Uncertain Reasoning ◽  
Reliability Modeling ◽  
Line System ◽  
Reliability Indices

Aiming at the limitations of traditional reliability analysis theory in multi-state system, a method for reliability modeling and assessment of a multi-state system based on Bayesian Network (BN) is proposed with the advantages of uncertain reasoning and describing multi-state of event. Through the case of cell production line system, in this paper we will discuss how to establish and construct a multi-state system model based on Bayesian network, and how to apply the prior probability and posterior probability to do the bidirectional inference analysis, and directly calculate the reliability indices of the system by means of prior probability and Conditional Probability Table (CPT) . Thereby we can do the qualitative and quantitative analysis of the multi-state system reliability, identify the weak links of the system, and achieve assessment of system reliability.

scholarly journals Alpha Is Not the False Alarm Rate: An Activity to Dispel a Common Statistical Misconception

2018 ◽  
Vol 46 (1) ◽  
pp. 72-79 ◽  
Author(s):  
W. Burt Thompson
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Research Finding ◽  
Statistical Power ◽  
Prior Probability ◽  
Statistical Significance ◽  
Statistical Test ◽  
Test Results ◽  
Web App ◽  
New Research

When a psychologist announces a new research finding, it is often based on a rejected null hypothesis. However, if that hypothesis is true, the claim is a false alarm. Many students mistakenly believe that the probability of committing a false alarm equals alpha, the criterion for statistical significance, which is typically set at 5%. Instructors should take specific steps to dispel this belief because it leads students to misinterpret statistical test results and it reinforces the more general misconception that results can be interpreted in isolation, without reference to theory or prior research. In the present study, students worked with a web app that shows how the false alarm rate is a function of the prior probability of an effect, statistical power, and alpha. Quiz scores suggest the activity helps correct the misconception, which can improve how students conduct and interpret research.

Estimating the prior probability of paternity from the results of exclusion tests

1984 ◽  
Vol 24 (2) ◽  
pp. 143-147 ◽  
Author(s):  
Aravinda Chakravarti ◽  
Ching Chun Li
Keyword(s):  
Prior Probability

scholarly journals Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2

JAMIA Open ◽  
2020 ◽  
Author(s):  
Qunfeng Dong ◽  
Xiang Gao
Keyword(s):  
Probability Distribution ◽  
Large Scale ◽  
Prior Probability ◽  
Probability Distributions ◽  
Single Point ◽  
Point Estimates ◽  
Specificity And Sensitivity ◽  
Large Scale Dataset ◽  
The Us ◽  
Antibody Tests

Abstract Accurate estimations of the seroprevalence of antibodies to severe acute respiratory syndrome coronavirus 2 need to properly consider the specificity and sensitivity of the antibody tests. In addition, prior knowledge of the extent of viral infection in a population may also be important for adjusting the estimation of seroprevalence. For this purpose, we have developed a Bayesian approach that can incorporate the variabilities of specificity and sensitivity of the antibody tests, as well as the prior probability distribution of seroprevalence. We have demonstrated the utility of our approach by applying it to a recently published large-scale dataset from the US CDC, with our results providing entire probability distributions of seroprevalence instead of single-point estimates. Our Bayesian code is freely available at https://github.com/qunfengdong/AntibodyTest.

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