A hierarchical Bayesian analysis of multiple order constraints in behavioral science

2018 ◽  
Author(s):  
◽  
Julia M. Haaf
Keyword(s):  
Model Comparison ◽  
Behavioral Science ◽  
Meta Analysis ◽  
Human Perception ◽  
Empirical Science ◽  
Single Experiment ◽  
Left Hand ◽  
Hierarchical Bayesian Analysis ◽  
Bayesian Model Comparison ◽  
The Individual

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Psychology is an empirical science, and oftentimes the main target of interest is an empirical effect. For example, we may be interested in human perception and ask participants to react to light spots flashing up on a screen as fast as they can. Psychologists typically ask whether, on average, participants respond faster to bright lights than to dim ones. In my dissertation, I attempt to extend this question on the individual participant's level: Does everyone react to bright lights faster than to dim ones? In case of perception, this seems reasonable: After accounting for sample noise, we probably would expect that indeed everyone is better at perceiving higher-signal visual stimuli. Yet, we may not expect that everyone throws a ball further with their right hand than their left hand. Clearly, left-handed people may not. And in other areas, we do not have any expectation of whether everyone truly shows an effect or not. In my dissertation, I provide the means of studying the "Does Everyone" question. I develop a set of statistical models including a model where some people show an effect while others show the opposite effect; a model where some people show an effect while others do not; and a model where all people show an effect. I provide a Bayesian model-comparison approach to quantify evidence for these theoretically motivated models. And, finally, I show how the modeling approach can be applied both in a single-experiment setting and in meta-analysis to quantify evidence across many studies.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Bayesian model comparison

2014 ◽  
pp. 101-117
Author(s):  
Michael D. Lee ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Bayesian Model Comparison

scholarly journals A complete search for redshift z ≳ 6.5 quasars in the VIKING survey

2020 ◽  
Vol 501 (2) ◽  
pp. 1663-1676
Author(s):  
R Barnett ◽  
S J Warren ◽  
N J G Cross ◽  
D J Mortlock ◽  
X Fan ◽  
...  
Keyword(s):  
Model Comparison ◽  
High Redshift ◽  
Detailed Comparison ◽  
Selection Function ◽  
Data Set ◽  
Bayesian Model Comparison ◽  
Fitting Method ◽  
Complete Search ◽  
Potential Contaminant ◽  
Ranked List

ABSTRACT We present the results of a new, deeper, and complete search for high-redshift 6.5 < z < 9.3 quasars over 977 deg2 of the VISTA Kilo-Degree Infrared Galaxy (VIKING) survey. This exploits a new list-driven data set providing photometry in all bands Z, Y, J, H, Ks, for all sources detected by VIKING in J. We use the Bayesian model comparison (BMC) selection method of Mortlock et al., producing a ranked list of just 21 candidates. The sources ranked 1, 2, 3, and 5 are the four known z > 6.5 quasars in this field. Additional observations of the other 17 candidates, primarily DESI Legacy Survey photometry and ESO FORS2 spectroscopy, confirm that none is a quasar. This is the first complete sample from the VIKING survey, and we provide the computed selection function. We include a detailed comparison of the BMC method against two other selection methods: colour cuts and minimum-χ2 SED fitting. We find that: (i) BMC produces eight times fewer false positives than colour cuts, while also reaching 0.3 mag deeper, (ii) the minimum-χ2 SED-fitting method is extremely efficient but reaches 0.7 mag less deep than the BMC method, and selects only one of the four known quasars. We show that BMC candidates, rejected because their photometric SEDs have high χ2 values, include bright examples of galaxies with very strong [O iii] λλ4959,5007 emission in the Y band, identified in fainter surveys by Matsuoka et al. This is a potential contaminant population in Euclid searches for faint z > 7 quasars, not previously accounted for, and that requires better characterization.

scholarly journals A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis

2018 ◽  
Vol 265 ◽  
pp. 271-278 ◽  
Author(s):  
Tyler B. Grove ◽  
Beier Yao ◽  
Savanna A. Mueller ◽  
Merranda McLaughlin ◽  
Vicki L. Ellingrod ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Visual Integration ◽  
Bayesian Model Comparison ◽  
Comparison Approach

Metastudie industrieller Energieflexibilität*/Meta-analysis of industrial energy flexibility - An approach for optimizing the identification of energy flexibility potentials

2017 ◽  
Vol 107 (09) ◽  
pp. 610-616
Author(s):  
S. Eisenhauer ◽  
F. Zimmermann ◽  
M. Reichart ◽  
P. Accordi ◽  
A. Prof. Sauer
Keyword(s):  
Production System ◽  
Meta Analysis ◽  
German Industry ◽  
Wird Eine ◽  
Bottom Up ◽  
Systematic Analysis ◽  
Industry Level ◽  
Large Spread ◽  
Industrial Energy ◽  
The Individual

Bisherige Studien über energetische Flexibilität in der deutschen Industrie weisen das vorhandene Flexibilitätspotenzial mit hoher Streuung aus. Diese Arbeit analysiert relevante Studien in Bezug auf deren Annahmen und Vorgehensweise. Aufbauend auf den bisherigen Vorgehensweisen wird ein Ansatz zur Erhebung der Daten im Produktionssystem vorgestellt. Des Weiteren wird eine Methode zur Aggregation der Daten hoch bis auf Branchenebene entwickelt.   Previous studies on the energetic flexibility of German industry show potentials with a large spread. Therefore, in this article, a systematic analysis of the individual studies and an evaluation of the indicated flexibility potentials are carried out. Based on the existing methods, a bottom-up approach for collecting the data in the production system and the aggregation up to the industry level is presented.

Uncertainty of prior and posterior model probability: Implications for interpreting Bayes factors

2021 ◽  
Author(s):  
John K. Kruschke
Keyword(s):  
Posterior Distribution ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayes Factor ◽  
Bayes Factors ◽  
Prior Model ◽  
Bayesian Hypothesis Testing ◽  
Bayesian Model Comparison ◽  
Posterior Model Probability ◽  
Model Probability

In most applications of Bayesian model comparison or Bayesian hypothesis testing, the results are reported in terms of the Bayes factor only, not in terms of the posterior probabilities of the models. Posterior model probabilities are not reported because researchers are reluctant to declare prior model probabilities, which in turn stems from uncertainty in the prior. Fortunately, Bayesian formalisms are designed to embrace prior uncertainty, not ignore it. This article provides a novel derivation of the posterior distribution of model probability, and shows many examples. The posterior distribution is useful for making decisions taking into account the uncertainty of the posterior model probability. Benchmark Bayes factors are provided for a spectrum of priors on model probability. R code is posted at https://osf.io/36527/. This framework and tools will improve interpretation and usefulness of Bayes factors in all their applications.

Combined-chain nested sampling for efficient Bayesian model comparison

2017 ◽  
Vol 70 ◽  
pp. 84-93 ◽  
Author(s):  
R. Wesley Henderson ◽  
Paul M. Goggans ◽  
Lei Cao
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Nested Sampling ◽  
Bayesian Model Comparison

scholarly journals Revisiting the Role of Place in Geographic Information Science

2018 ◽  
Vol 7 (9) ◽  
pp. 364 ◽  
Author(s):  
Helena Merschdorf ◽  
Thomas Blaschke
Keyword(s):  
Information Science ◽  
Human Perception ◽  
Geographic Information Science ◽  
Geographic Information ◽  
Human Environment ◽  
Subjective Data ◽  
Main Research ◽  
The Social ◽  
The Individual

Although place-based investigations into human phenomena have been widely conducted in the social sciences over the last decades, this notion has only recently transgressed into Geographic Information Science (GIScience). Such a place-based GIS comprises research from computational place modeling on one end of the spectrum, to purely theoretical discussions on the other end. Central to all research that is concerned with place-based GIS is the notion of placing the individual at the center of the investigation, in order to assess human-environment relationships. This requires the formalization of place, which poses a number of challenges. The first challenge is unambiguously defining place, to subsequently be able to translate it into binary code, which computers and geographic information systems can handle. This formalization poses the next challenge, due to the inherent vagueness and subjectivity of human data. The last challenge is ensuring the transferability of results, requiring large samples of subjective data. In this paper, we re-examine the meaning of place in GIScience from a 2018 perspective, determine what is special about place, and how place is handled both in GIScience and in neighboring disciplines. We, therefore, adopt the view that space is a purely geographic notion, reflecting the dimensions of height, depth, and width in which all things occur and move, while place reflects the subjective human perception of segments of space based on context and experience. Our main research questions are whether place is or should be a significant (sub)topic in GIScience, whether it can be adequately addressed and handled with established GIScience methods, and, if not, which other disciplines must be considered to sufficiently account for place-based analyses. Our aim is to conflate findings from a vast and dynamic field in an attempt to position place-based GIS within the broader framework of GIScience.

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