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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
2.
Post Hoc Bayesian Analyses/ Reply Aberegg, Scott K; Ferreira, David; Meyer, Nicolas ...
JAMA : the journal of the American Medical Association,
04/2019, Volume:
321, Issue:
16
Journal Article
Reward is thought to attenuate forgetting through the automatic effect of dopamine on hippocampal memory traces. Here we report a conceptual replication of previous results where we did not observe ...this effect of reward. Participants encoded eight lists of pictures and recalled picture content immediately or the next day. They were informed that they could gain monetary reward for recalling the pictures, with the level of reward indicated through the frame surrounding the picture. Reward was manipulated both within and across lists. Bayesian statistics found moderate evidence for the null hypothesis that reward does not modulate forgetting in human free recall.
Los Bosques Secos Estacionales Neotropicales (BSEN) en Sudamérica presentan una distribución fragmentada en cuatro núcleos mayores. La hipótesis del Arco Pleistocénico explica el patrón de ...distribución de un elevado número de especies arbóreas en varios fragmentos de los BSEN. En el primer estudio filogeográfico de amplio rango realizado en los BSEN se analizaron poblaciones de Astronium urundeuva, una especie arbórea confinada a estos bosques. A partir de los datos genotípicos obtenidos con nueve microsatélites específicos en 1.124 individuos de A. urundeuva, se reanalizaron los datos mediante Approximate Bayesian Computation (ABC) para determinar la hipótesis filogeográfica más probable que explique su evolución. Considerando los grupos genéticos Central (CE), Noreste (NE) y Suroeste (SW), identificados mediante un análisis Bayesiano de la estructura genética, se testaron cuatro escenarios considerando diferencias en el tiempo de divergencia, mientras que dentro de cada escenario se plantearon cuatro modelos de divergencia. Se identificó el modelo mejor respaldado dentro de cada escenario y luego entre los escenarios. Se estimaron los valores de los parámetros desde la distribución posterior del modelo más probable. El modelo más probable considera divergencia entre los grupos más distantes NE y SW en el último máximo glacial y divergencia posterior del grupo CE desde el NE en el Holoceno. Estos resultados confirman la hipótesis del Arco Pleistocénico como principal explicación para la distribución actual de los BSEN, basado en la filogeografía de A. urundeuva.
Variational Inference: A Review for Statisticians Blei, David M.; Kucukelbir, Alp; McAuliffe, Jon D.
Journal of the American Statistical Association,
06/2017, Volume:
112, Issue:
518
Journal Article
Peer reviewed
Open access
One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference ...about unknown quantities as a calculation involving the posterior density. In this article, we review variational inference (VI), a method from machine learning that approximates probability densities through optimization. VI has been used in many applications and tends to be faster than classical methods, such as Markov chain Monte Carlo sampling. The idea behind VI is to first posit a family of densities and then to find a member of that family which is close to the target density. Closeness is measured by Kullback-Leibler divergence. We review the ideas behind mean-field variational inference, discuss the special case of VI applied to exponential family models, present a full example with a Bayesian mixture of Gaussians, and derive a variant that uses stochastic optimization to scale up to massive data. We discuss modern research in VI and highlight important open problems. VI is powerful, but it is not yet well understood. Our hope in writing this article is to catalyze statistical research on this class of algorithms. Supplementary materials for this article are available online.
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BFBNIB, GIS, IJS, INZLJ, KISLJ, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK, ZRSKP
Abstract
This paper focuses on the requirements of operational efficiency evaluation for UAV cluster, and establishes an evaluation index system for UAV cluster operations, and establishes evaluation ...index system from the aspects of cooperative attack capability, autonomous reconnaissance capability, instant messaging capability, rapid planning capability and autonomous formation capability. The evaluation network model is built by using Bayesian network, and the corresponding prior and conditional probabilities are calculated. The effectiveness values of each capability are calculated and verified by experiments. This paper provides an applicable quantitative evaluation method for UAV cluster operational effectiveness evaluation.
Bayesian networks help us model and understand the many variables that inform our decision‐making processes. Anthony C. Constantinou and Norman Fenton explain how they work, how they are built and ...the pitfalls to avoid along the way
Bayesian networks help us model and understand the many variables that inform our decision‐making processes. Anthony C. Constantinou and Norman Fenton explain how they work, how they are built and the pitfalls to avoid along the way.
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BFBNIB, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
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