scholarly journals An independent search for annual modulation and its significance in ANAIS-112 data

2020 ◽  
Vol 2020 (9) ◽  
Author(s):  
Aditi Krishak ◽  
Shantanu Desai
Keyword(s):  
Information Theory ◽  
Dark Matter ◽  
Null Hypothesis ◽  
Bayesian Model ◽  
Model Comparison ◽  
Information Criteria ◽  
The Other ◽  
Annual Modulation ◽  
Bayesian Model Comparison ◽  
Exponential Fit

Abstract We perform an independent search for sinusoidal-based modulation in the recently released ANAIS-112 data, which could be induced by dark matter scatterings. We then evaluate this hypothesis against the null hypothesis that the data contain only background, using four different model comparison techniques. These include frequentist, Bayesian, and two information theory-based criteria (Akaike and Bayesian information criteria). This analysis was done on both the residual data (by subtracting the exponential fit obtained from the ANAIS-112 Collaboration) as well as the total (non-background subtracted) data. We find that according to the Bayesian model comparison test, the null hypothesis of no modulation is decisively favored over a cosine-based annual modulation for the non-background subtracted dataset in the 2–6 keV energy range. None of the other model comparison tests decisively favor any one hypothesis over another. This is the first application of Bayesian and information theory techniques to test the annual modulation hypothesis in ANAIS-112 data, extending our previous work on the DAMA/LIBRA and COSINE-100 data. Our analysis codes have also been made publicly available.

scholarly journals Game violence, game difficulty, and 2D:4D digit ratio as predictors of aggressive behavior

2015 ◽  
Author(s):  
◽  
Joseph B. Hilgard
Keyword(s):  
Aggressive Behavior ◽  
Null Hypothesis ◽  
Bayesian Model ◽  
Model Comparison ◽  
Digit Ratio ◽  
Experimental Control ◽  
Bayesian Model Comparison ◽  
Statistical Precision ◽  
Alternative Hypotheses ◽  
Game Violence

Previous research efforts have sought to determine whether violent video games cause increases in aggressive behavior. The resulting literature is controversial. Those finding the effect may not have the desired degree of experimental control, while those not finding the effect may not always have the required statistical precision. Game difficulty is also thought to potentially cause changes in aggression. Finally, prenatal testosterone exposure, as measured by the ratio of lengths of the index and ring fingers (2D:4D digit ratio), is thought to cause dispositional increases in aggressive behavior. In the present research, game violence and game difficulty were manipulated in a tightly-controlled modified-game paradigm. Participants played a game, were provoked, and then had an opportunity to aggress against another participant in the study. Neither game violence nor game difficulty was found to influence aggressive behavior in a theory-consistent way, and Bayesian model comparison techniques favored the null hypothesis over all theory-derived alternative hypotheses. Similarly, 2D:4D ratio did not predict aggressive behavior, either alone or in interaction with the other study variables. The results suggest that brief exposure to violent or difficult games does not influence aggressive behavior when game stimuli are closely matched, and furthermore, that 2D:4D digit ratio does not predict aggression.

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 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

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 Capturing Ordinal Theoretical Constraint in Psychological Science

2018 ◽  
Author(s):  
Julia M. Haaf ◽  
Fayette Klaassen ◽  
Jeffrey Rouder
Keyword(s):  
Social Sciences ◽  
Linear Model ◽  
Bayesian Model ◽  
Model Comparison ◽  
Social Scientists ◽  
Psychological Science ◽  
Bayesian Model Comparison ◽  
Comparison Approach ◽  
The Social ◽  
Ordinal Level

Most theories in the social sciences are verbal and provide ordinal-level predictions for data. For example, a theory might predict that performance is better in one condition than another, but not by how much. One way of gaining additional specificity is to posit many ordinal constraints that hold simultaneously. For example a theory might predict an effect in one condition, a larger effect in another, and none in a third. We show how common theoretical positions naturally lead to multiple ordinal constraints. To assess whether multiple ordinal constraints hold in data, we adopt a Bayesian model comparison approach. The result is an inferential system that is custom-tuned for the way social scientists conceptualize theory, and that is more intuitive and informative than current linear-model approaches.

scholarly journals Intermittent Hormone Therapy Models Analysis and Bayesian Model Comparison for Prostate Cancer

2021 ◽  
Vol 84 (1) ◽  
Author(s):  
S. Pasetto ◽  
H. Enderling ◽  
R. A. Gatenby ◽  
R. Brady-Nicholls
Keyword(s):  
Prostate Cancer ◽  
Bayesian Model ◽  
Model Comparison ◽  
Locally Advanced ◽  
Basin Of Attraction ◽  
Specific Antigen ◽  
Resistance Mechanisms ◽  
Patient Specific ◽  
Response Dynamics ◽  
Bayesian Model Comparison

AbstractThe prostate is an exocrine gland of the male reproductive system dependent on androgens (testosterone and dihydrotestosterone) for development and maintenance. First-line therapy for prostate cancer includes androgen deprivation therapy (ADT), depriving both the normal and malignant prostate cells of androgens required for proliferation and survival. A significant problem with continuous ADT at the maximum tolerable dose is the insurgence of cancer cell resistance. In recent years, intermittent ADT has been proposed as an alternative to continuous ADT, limiting toxicities and delaying time-to-progression. Several mathematical models with different biological resistance mechanisms have been considered to simulate intermittent ADT response dynamics. We present a comparison between 13 of these intermittent dynamical models and assess their ability to describe prostate-specific antigen (PSA) dynamics. The models are calibrated to longitudinal PSA data from the Canadian Prospective Phase II Trial of intermittent ADT for locally advanced prostate cancer. We perform Bayesian inference and model analysis over the models’ space of parameters on- and off-treatment to determine each model’s strength and weakness in describing the patient-specific PSA dynamics. Additionally, we carry out a classical Bayesian model comparison on the models’ evidence to determine the models with the highest likelihood to simulate the clinically observed dynamics. Our analysis identifies several models with critical abilities to disentangle between relapsing and not relapsing patients, together with parameter intervals where the critical points’ basin of attraction might be exploited for clinical purposes. Finally, within the Bayesian model comparison framework, we identify the most compelling models in the description of the clinical data.

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