scholarly journals Subjective Bayesian testing using calibrated prior probabilities

2019 ◽  
Vol 33 (4) ◽  
pp. 861-893
Author(s):  
Dan J. Spitzner
Keyword(s):  
Prior Probabilities ◽  
Bayesian Testing

Investigating the Ease-of-Retrieval Effect in an Eyewitness Context

2019 ◽  
Author(s):  
Roy Groncki ◽  
Jennifer L Beaudry ◽  
James D. Sauer
Keyword(s):  
Cognitive Processes ◽  
Task Difficulty ◽  
Memory Studies ◽  
Relevant Information ◽  
Perceived Quality ◽  
Expected Effect ◽  
Bayesian Testing ◽  
Finding Study ◽  
Identification Confidence

The way in which individuals think about their own cognitive processes plays an important role in various domains. When eyewitnesses assess their confidence in identification decisions, they could be influenced by how easily relevant information comes to mind. This ease-of-retrieval effect has a robust influence on people’s cognitions in a variety of contexts (e.g., attitudes), but it has not yet been applied to eyewitness decisions. In three studies, we explored whether the ease with which eyewitnesses recall certain memorial information influenced their identification confidence assessments and related testimony-relevant judgements (e.g., perceived quality of view). We manipulated the number of reasons participants gave to justify their identification (Study 1; N = 343), and also the number of instances they provided of a weak or strong memory (Studies 2a & 2b; Ns = 350 & 312, respectively). Across the three studies, ease-of-retrieval did not affect eyewitnesses’ confidence or other testimony-relevant judgements. We then tried—and failed—to replicate Schwarz et al.’s (1991) original ease-of-retrieval finding (Study 3; N = 661). In three of the four studies, ease-of-retrieval had the expected effect on participants’ perceived task difficulty; however, frequentist and Bayesian testing showed no evidence for an effect on confidence or assertiveness ratings.

scholarly journals Minimax-Optimal Bounds for Detectors Based on Estimated Prior Probabilities

2012 ◽  
Vol 58 (9) ◽  
pp. 6101-6109 ◽  
Author(s):  
Jiantao Jiao ◽  
Lin Zhang ◽  
Robert D. Nowak
Keyword(s):  
Prior Probabilities ◽  
Optimal Bounds

scholarly journals Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part I: Theory and Scheme

2013 ◽  
Vol 141 (6) ◽  
pp. 1737-1760 ◽  
Author(s):  
Thomas Sondergaard ◽  
Pierre F. J. Lermusiaux
Keyword(s):  
Data Assimilation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Planck Equation ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Field Equations ◽  
Dynamical Equations ◽  
Prior Probabilities

Abstract This work introduces and derives an efficient, data-driven assimilation scheme, focused on a time-dependent stochastic subspace that respects nonlinear dynamics and captures non-Gaussian statistics as it occurs. The motivation is to obtain a filter that is applicable to realistic geophysical applications, but that also rigorously utilizes the governing dynamical equations with information theory and learning theory for efficient Bayesian data assimilation. Building on the foundations of classical filters, the underlying theory and algorithmic implementation of the new filter are developed and derived. The stochastic Dynamically Orthogonal (DO) field equations and their adaptive stochastic subspace are employed to predict prior probabilities for the full dynamical state, effectively approximating the Fokker–Planck equation. At assimilation times, the DO realizations are fit to semiparametric Gaussian Mixture Models (GMMs) using the Expectation-Maximization algorithm and the Bayesian Information Criterion. Bayes’s law is then efficiently carried out analytically within the evolving stochastic subspace. The resulting GMM-DO filter is illustrated in a very simple example. Variations of the GMM-DO filter are also provided along with comparisons with related schemes.

Prior probabilities modulate cortical surprise responses: A study of event-related potentials

2016 ◽  
Vol 106 ◽  
pp. 78-89 ◽  
Author(s):  
Caroline Seer ◽  
Florian Lange ◽  
Moritz Boos ◽  
Reinhard Dengler ◽  
Bruno Kopp
Keyword(s):  
Event Related Potentials ◽  
Prior Probabilities ◽  
Related Potentials

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood

Psychometrika ◽  
2017 ◽  
Vol 83 (2) ◽  
pp. 321-332 ◽  
Author(s):  
George Karabatsos
Keyword(s):  
Conjoint Measurement ◽  
Bayesian Testing

Optimal ambiguous discrimination between states given by two projections

2014 ◽  
Vol 64 (1) ◽  
Author(s):  
Krzysztof Kaniowski
Keyword(s):  
Hilbert Space ◽  
Quantum States ◽  
Prior Probabilities ◽  
Optimal Measurements

AbstractLet P 0 and P 1 be projections in a Hilbert space H. We shall construct a class of optimal measurements for the problem of discrimination between quantum states $$\rho _i = \tfrac{1} {{\dim P_i }}P_i$$, with prior probabilities π 0 and π 1. The probabilities of failure for such measurements will also be derived.

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