Results 101 to 110 of about 167,711 (260)

Testing MCMC code

, 2014
Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively. We outline several strategies for testing the correctness of MCMC algorithms.
Grosse, Roger B., Duvenaud, David K.
openaire   +2 more sources

Phenotypic Subtypes of Obstructive Eustachian Tube Dysfunction as Defined by Cluster Analysis

The Laryngoscope, EarlyView.
Obstructive ETD encompasses five clinically distinct phenotypes, ranging from mild, post‐upper respiratory infection presentations to chronic, bilateral disease driven by sinusitis and reflux. These were identified through hierarchical cluster analysis of 490 patients using seven key clinical variables.
Jenilkumar H. Patel, Duncan G. J. Green, Manal S. Malik, Steven X. Wang, Edward D. McCoul   +4 more
wiley   +1 more source

MCMC for GLMMs

, 2022
Generalized linear mixed models (GLMMs) are often used for analyzing correlated non-Gaussian data. The likelihood function in a GLMM is available only as a high dimensional integral, and thus closed-form inference and prediction are not possible for GLMMs.
openaire   +2 more sources

A Short Version of Carers' Quality of Life Questionnaire for Parkinsonism: Data from Progressive Supranuclear Palsy Network

Movement Disorders Clinical Practice, EarlyView.
Abstract Background and objectives Caregivers of progressive supranuclear palsy (PSP) patients frequently show significant distress. The Parkinsonism Carers quality of life (QoL) (PQoL Carer) is a valid tool evaluating the effect of PSP on caregivers' QoL.
Arianna Cappiello, Giovanna Calandra‐Buonaura, Roberto Ceravolo, Saverio Coralli, Sofia Cuoco, Eleonora del Prete, Francesca di Biasio, Daniela Frosini, Vittorio Gualtieri, Davide Mascioli, Luisa Sambati, Tommaso Schirinzi, Alessandro Stefani, Elisa Umbertini, Marco Aiello, Matteo Costanzo, Rosa de Micco, Giovanni Fabbrini, Gaetano Failla, Chiara Longo, Maria Chiara Malaguti, Nicola Modugno, Enrica Olivola, Noemi Maria Carola Russo, Alessandro Tessitore, Andrea Ciammola, Carlo Colosimo, Anna de Rosa, Laura de Togni, Alessio di Fonzo, Vincenzo Moschella, Andrea Pilotto, Francesca Spagnolo, Roberta Zangaglia, Paolo Barone, Marina Picillo, the PSP‐NET study group, Maria Autuori, Anna Rosa Avallone, Ilaria Cani, Giannicola Carrozzo, Nicola Grotteschi, Maria Mancini, Clara Simonetta, Maria Francesca Tepedino, Henri Zenuni, Simone Aloisio, Ruggero Bacchin, Lorena Belli, Daniele Belvisi, Francesco Marchet, Sara Pietracupa, Sara Satolli, Maria Concetta Altavista, Marta Filidei, Giulia Lazzeri, Simone Malaspina, Alessando Padovani, Gabriele Riccio, Andrea Mirko Tedesco, Susanne Buechner, Laura Bonanni, Marianna Capecci, Maria Gabriella Ceravolo, Massimo Cincotta, Maria Francesca de Pandis, Raffaella di Giacopo, Lorenza Leonardi, Leonardo Lopiano, Fabrizio Stocchi, Laura Vacca, Sara Varanese, Maristella Piccininni, Maurizio Zibetti   +73 more
wiley   +1 more source

Extending the hyper‐logistic model to the random setting: New theoretical results with real‐world applications

Mathematical Methods in the Applied Sciences, EarlyView.
We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés, Ana Navarro‐Quiles, Sorina Madalina Sferle   +2 more
wiley   +1 more source

Uncertainty Quantification of Geophysical and Hydrologic Parameters Estimated From Borehole Nuclear Magnetic Resonance Data

Journal of Geophysical Research: Machine Learning and Computation
Borehole nuclear magnetic resonance (bNMR) data are typically used to infer in situ hydrologic properties. Partial water content as a function of pore size is estimated by fitting the measured NMR response to a multi‐exponential T2 distribution, and the ...
Burke J. Minsley, Stephanie N. Phillips, Stephanie R. James   +2 more
doaj   +1 more source

MinDet1: A deep learning-enabled approach for plagioclase textural studies

Volcanica
Quantitative textural attributes, such as crystal size distributions or aspect ratios, provide important constraints on the thermal history of rocks.
Norbert Toth, John Maclennan
doaj   +1 more source

Ellipsoid‐Based Interval‐Type Uncertainty Model Updating Based on Riemannian Manifold and Gaussian Process Model

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Modern engineering systems require advanced uncertainty‐aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive‐Definite (SPD) matrix ...
Yanhe Tao, Qintao Guo, Jin Zhou, Cheng Yi   +3 more
wiley   +1 more source

Amanita theophili sp. nov. (Amanitaceae) from central Mexico

Nordic Journal of Botany, EarlyView.
Amanita theophili sp. nov., a member of Amanita sect. Amidella (Amanitaceae), is described from temperate pine‐oak forests in Morelos, central Mexico. Morphological features and phylogenetic analyses based on ITS and 28S rDNA sequences confirm its distinct taxonomic status. The new species is morphologically similar to A. peckiana and A.
Evangelina Pérez‐Silva, Edith Garay‐Serrano   +1 more
wiley   +1 more source

To MCMC or not to MCMC: Evaluating non-MCMC methods for Bayesian penalized regression

Markov Chain Monte Carlo (MCMC) sampling is computationally expensive, especially for complex models. Alternative methods make simplifying assumptions about the posterior to reduce computational burden, but their impact on predictive performance remains unclear.
van Leeuwen, Florian D., van Erp, Sara
openaire   +2 more sources