Results 71 to 80 of about 7,662,414 (297)

A hierarchical Bayesian model for syntactic priming [PDF]

CoRR
6 pages; accepted to CogSci ...
Xu, Weijie, Futrell, Richard
openaire   +3 more sources

Advances in Bio‐Integrated Edge Computing Systems for Smart and Connected Health

Advanced Materials, EarlyView.
This review explores advances in bio‐integrated edge computing for healthcare, focusing on co‐designing computing, sensing, and energy strategies to overcome integration and power challenges for optimized, decentralized operation. ABSTRACT Recent advances in bio‐integrated healthcare technologies have enabled continuous, high‐resolution monitoring and ...
Gyuwon Kim, Seungwon Yoo, Tae Woog Kang, Yunuo Huang, Woon‐Hong Yeo   +4 more
wiley   +1 more source

A hierarchical Bayesian approach for calibration of stochastic material models

Data-Centric Engineering, 2021
This article recasts the traditional challenge of calibrating a material constitutive model into a hierarchical probabilistic framework. We consider a Bayesian framework where material parameters are assigned distributions, which are then updated given ...
Nikolaos Papadimas, Timothy Dodwell
doaj   +1 more source

Sensitivity Analysis for Bayesian Hierarchical Models

Bayesian Analysis, 2015
Prior sensitivity examination plays an important role in applied Bayesian analyses. This is especially true for Bayesian hierarchical models, where interpretability of the parameters within deeper layers in the hierarchy becomes challenging. In addition, lack of information together with identifiability issues may imply that the prior distributions for
Roos, Małgorzata, Martins, Thiago G., Held, Leonhard, Rue, Håvard   +3 more
openaire   +4 more sources

A hierarchical Bayesian model of pitch framing [PDF]

Journal of Quantitative Analysis in Sports, 2017
Abstract Since the advent of high-resolution pitch tracking data (PITCHf/x), many in the sabermetrics community have attempted to quantify a Major League Baseball catcher’s ability to “frame” a pitch (i.e. increase the chance that a pitch is a called as a strike).
Sameer K. Deshpande, Abraham Wyner
openaire   +2 more sources

Efficient utility-based clustering over high dimensional partition spaces [PDF]

, 2009
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ, Millar, A. J., Millar, AJ, Liverani, Silvia, Smith, J. Q., Liverani, S, Anderson, Paul E., Anderson, PE, Edwards, Kieron D., Edwards, KD   +9 more
core   +1 more source

Multi‐Parametric Fluorescence Spectroscopy for the Detection and Characterization of Plant‐Derived Protein Residues on 316L Stainless Steel

Advanced Materials Interfaces, EarlyView.
UV–vis fluorescence spectroscopy enables sensitive monitoring of plant protein adsorption on AISI 316L stainless steel under food‐processing‐relevant conditions. Spectral deconvolution reveals adsorption‐induced changes associated with protein–surface interactions and conformational alterations, demonstrating the potential of the technique for ...
Alexander Khort, Erik Jansson, Gunilla Herting, Eva Blomberg, Kirsti Viljaste, Ahana Mukherjee, Inger Odnevall   +6 more
wiley   +1 more source

Semiparametric Bayesian inference in multiple equation models [PDF]

, 2005
This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components.
Koop, G.M., Poirier, D., Poirier, Dale J., Tobias, Justin, Tobias, J., Koop, Gary   +5 more
core   +3 more sources

Hierarchical Bayesian conjoint models incorporating measurement uncertainty

, 2008
Conjoint analysis, Weighted regression models, Measurement uncertainty, Confidence, Interval data, Hierarchical Bayesian models,
De Bruyn, Arnaud, Huizingh, Eelko K. R. E.; id_orcid, John Liechty, Arnaud Bruyn, Fong, Duncan K. H., Liechty, John C., Duncan Fong, Eelko Huizingh, Huizingh, Eelko K. R. E.   +8 more
core   +1 more source

Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods [PDF]

, 2014
We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions ...
Li, S., M. Rigby, Ganesan, Anita Lakshmi, S. Park, Fraser, P. J., S. Li, S. J. O'Doherty, P. K. Salameh, Prinn, Ronald G., L. P. Steele, Ganesan, A. L., O'Doherty, Simon, A. Zammit-Mangion, Krummel, P. B., P. B. Krummel, Muehle, J., Zammit-Mangion, A., Prinn, R. G., Weiss, R. F., Muhle, Jens, Steele, L. P., Rigby, M., Manning, A. J., Kim, K. -R., A. J. Manning, A. L. Ganesan, R. F. Weiss, Park, S., Manning, Alistair J., C. M. Harth, P. J. Fraser, Harth, C. M., Salameh, P. K., K.-R. Kim, O'Doherty, S. J., J. Mühle, R. G. Prinn, Kim, K.-R.   +37 more
core   +2 more sources