Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Myrthe Veenman, Angelika M. Stefan, Julia M. Haaf
With the recent development of easy-to-use tools for Bayesian analysis, psychologists have started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide an intuitive account of inter- and intraindividual variability and are particularly suited for the evaluation of repeated-measures designs.…
Li Zhu, Dennis M Gorman, Scott Horel
Background Ecologic studies have shown a relationship between alcohol outlet densities, illicit drug use and violence. The present study examined this relationship in the City of Houston, Texas, using a sample of 439 census tracts. Neighborhood sociostructural covariates, alcohol outlet density, drug crime density and…
James A. Fordyce, Zachariah Gompert, Matthew L. Forister, Chris C. Nice + 1 more
'Chris C. Nice' 'Enrico Scalas'] Many ecological studies use the analysis of count data to arrive at biologically meaningful inferences. Here, we introduce a hierarchical Bayesian approach to count data. This approach has the advantage over traditional approaches in that it directly estimates the parameters of interest…
Maura Mezzetti, Colleen P. Ryan, Priscilla Balestrucci, Francesco Lacquaniti + 1 more
'Francesco Lacquaniti' 'Alessandro Moscatelli'] Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods. The aim of this article is to revisit hierarchical models in a Bayesian framework. Bayesian models have…
Udo Boehm, Maarten Marsman, Dora Matzke, Eric-Jan Wagenmakers
Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment.…
Prasenjit Ghosh, Anirban Bhattacharya, Debdeep Pati
In this work, we offer a thorough analytical investigation into the role of shared hyperparameters in a hierarchical Bayesian model, examining their impact on information borrowing and posterior inference. Our approach is rooted in a non-asymptotic framework, where observations are drawn from a mixed-effects model, and…
Oliver Dyjas, Raoul P. P. P. Grasman, Ruud Wetzels, Han L. J. van der Maas + 1 more
'Han L. J. van der Maas' 'Eric-Jan Wagenmakers'] People generally prefer their initials to the other letters of the alphabet, a phenomenon known as the name-letter effect. This effect, researchers have argued, makes people move to certain cities, buy particular brands of consumer products, and choose particular…
Bertrand Clarke, Dean Dustin
> Abstract: We use the law of total variance to generate multiple expressions for the posterior predictive variance in Bayesian hierarchical models. These expressions are sums of terms involving conditional expectations and conditional variances. Since the posterior predictive variance is fixed given the hierarchical…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Irving Gómez-Méndez, Chainarong Amornbunchornvej
- One-shirt-size policy cannot handle poverty issues well since each region has its unique challenge while having custom-made policy for each region separately is unrealistic in term of resources. - In this work, hierarchical models are deployed to explain income and poverty-related variables in the multi-resolution…
Eric J. Ma, Arkadij Kummer
We present a case study applying hierarchical Bayesian estimation on high throughput protein melting point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in…
Laura Azzimonti, Giorgio Corani, Marco Zaffalon
—We present a novel approach for estimating conditional probability tables, based on a joint, rather than independent, estimate of the conditional distributions belonging to the same table. We derive exact analytical expressions for the estimators and we analyse their properties both analytically and via simulation. We…
Inesh Munaweera, Les N. Harris, Jean-Sébastien Moore, Ross F. Tallman + 6 more
Hierarchical modelling is frequently used to model ecological processes because of its ability to handle complex ecological phenomena by decomposing them into naturally explainable sub-models. Hierarchical Bayesian approaches have gained widespread use in health, social, and environmental sciences, including in the…
Ritabrata Dutta, Paul Blomstedt, Samuel Kaski
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally prohibitive if the sourcespecific data models are complex, or if the number of sources is very large. To facilitate computation, we propose…
J. Demšar, Grega Repovš, Erik Štrumbelj
Research in psychology generates interesting data sets and unique statistical modelling tasks. However, these tasks, while important, are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to those that…
Authors not listed
The reaction H₂ + OH → H₂O + H is fundamental to hydrogen combustion, atmospheric chemistry, and energy systems. Despite numerous experimental studies, comprehensive statistical comparison with modern uncertainty quantification has been lacking. This study presents a systematic analysis of ten independent kinetic…
Rishikesh U. Kulkarni, Catherine L. Wang, Carolyn R. Bertozzi
We report Hierarch, a Python package to perform hypothesis tests and compute confidence intervals on hierarchical experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate…
Calonico, Sebastian, Galiani, Sebastian
Empirical research in the social and medical sciences frequently involves testing multiple hypotheses simultaneously, increasing the risk of false positives due to chance. Classical multiple testing procedures, such as the Bonferroni correction, control the family-wise error rate (FWER) but tend to be overly…
Colin J. Carlson
Classification and regression tree methods, like random forests (RF) or boosted regression trees (BRT), are one of the most popular methods of mapping species distributions. Bayesian additive regression trees (BARTs) are a relatively new alternative to other popular regression tree approaches. Whereas BRT iteratively…
Arthur Newbury
Estimating underlying cooccurrence relationships between pairs of species has long been a challenging task in ecology as the extent to which species actually cooccur is partially dependent on their prevalences. While recent work has taken large steps towards solving this problem, the next question is how to assess the…
Authors not listed
Occupational chemical hazards pose profound risks to chemists in laboratory and industrial settings, encompassing acute and chronic exposures that imperil sensory organs (e.g., ocular, auditory, olfactory, dermal) and vital physiological systems. This manuscript delineates a multifaceted, innovative protocol suite…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…