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Search · four archives
25 papers · ranked by Valyu relevance
Juan Sosa, Carlos Martínez, Cruz, Danna
This paper offers a comprehensive introduction to Bayesian inference, combining historical context, theoretical foundations, and core analytical examples. Beginning with Bayes' theorem and the philosophical distinctions between Bayesian and frequentist approaches, we develop the inferential framework for estimation…
Stephen Wu, Panagiotis Angelikopoulos, James L. Beck, Petros Koumoutsakos
'Petros Koumoutsakos'] We classify two types of Hierarchical Bayesian Model found in the literature as Hierarchical Prior Model (HPM) and Hierarchical Stochastic Model (HSM). Then, we focus on studying the theoretical implications of the HSM. Using examples of polynomial functions, we show that the HSM is capable of…
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
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.…
Payam Piray, Amir Dezfouli, Tom Heskes, Michael J. Frank + 1 more
Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate…
Woo-Young Ahn, Nathaniel Haines, Lei Zhang
Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there…
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…
Irving Gómez-Méndez, Chainarong Amornbunchornvej
One-shirt-size policy cannot handle poverty issues well since each area has its unique challenges, while having a custom-made policy for each area separately is unrealistic due to limitation of resources as well as having issues of ignoring dependencies of characteristics between different areas. In this work, we…
Amanda H. Pendegraft, Boyi Guo, Nengjun Yi, Erwin G. Zoetendal
The analyses of large volumes of metagenomic data extracted from aggregate populations of microscopic organisms residing on and in the human body are advancing contemporary understandings of the integrated participation of microbes in human health and disease. Next generation sequencing technology facilitates said…
Simon Dirmeier, Niko Beerenwinkel
Genetic perturbation screening is an experimental method in biology to study cause and effect relationships between different biological entities. However, knocking out or knocking down genes is a highly error-prone process that complicates estimation of the effect sizes of the interventions. Here, we introduce a…
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…
Camilla van Geen, Raphael T. Gerraty
Reinforcement learning models have been used extensively to capture learning and decision-making processes in humans and other organisms. One essential goal of these computational models is the generalization to new sets of observations. Extracting parameters that can reliably predict out-of-sample data can be…
Se Yoon Lee
Nonlinear mixed effects models have become a standard platform for analysis when data is in the form of continuous and repeated measurements of subjects from a population of interest, while temporal profiles of subjects commonly follow a nonlinear tendency. While frequentist analysis of nonlinear mixed effects models…
Jonathan H. Huggins, Joshua B. Tenenbaum
Common statistical practice has shown that the full power of Bayesian methods is not realized until hierarchical priors are used, as these allow for greater "robustness" and the ability to "share statistical strength." Yet it is an ongoing challenge to provide a learning-theoretically sound formalism of such notions…
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…
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…
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…
Jie Pu, Di Fang, Jeffrey R. Wilson
Background The analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random component. Since the joint distribution of the observations is…
Daniel F. Linder, Viral Panchal
In this paper we describe a Bayesian hierarchical model termed ‘PMMLogit’ for classification and model selection in high-dimensional settings with binary phenotypes as outcomes. Posterior computation in the logistic model is known to be computationally demanding due to its non-conjugacy with common priors. We combine a…
Dorsa Mohammadi Arezooji
A hierarchical logistic regression Bayesian model is proposed and implemented in R to model the probability of patient improvement corresponding to any given dosage of a certain drug. RStan is used to obtain samples from the posterior distributions via Markov Chain Monte-Carlo (MCMC) sampling. The effects of selecting…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
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…
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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…