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Evaluating the impact of Hazelwood mine fire event on students' educational development with Bayesian interrupted time-series hierarchical meta-regression [article]

Caroline X Gao, Jonathan C. Broder, Sam Brilleman, Emily Berger, Jill Ikin, Catherine L. Smith, Tim C.H. Campbell, Rory Wolfe, Fay Johnston, Yuming Guo, Matthew Carroll
2021 medRxiv   pre-print
Using an interrupted time-series design, the model estimated immediate effects and post-interruption trend differences with full Bayesian statistical inference.  ...  Methods: A new method, Bayesian hierarchical meta-regression, was developed to evaluate the impact of the 2014 Hazelwood mine fire (a six-week fire event in Australia) using only aggregated school-level  ...  We also like to acknowledge Prof Rob Hyndman for his generous sharing of the Rmarkdown LaTex template (https://github.com/robjhyndman/ MonashEBSTemplates) for writing this paper in the Rmarkdown environment  ... 
doi:10.1101/2021.03.28.21254516 fatcat:g4lqq5kf3zd55klclg2dy6ptfa

Modelling and Optimizing the Process of Learning Mathematics [chapter]

Tanja Käser, Alberto Giovanni Busetto, Gian-Marco Baschera, Juliane Kohn, Karin Kucian, Michael von Aster, Markus Gross
2012 Lecture Notes in Computer Science  
This paper introduces a computer-based training program for enhancing numerical cognition aimed at children with developmental dyscalculia.  ...  Domain knowledge is represented with a dynamic Bayesian network on which the mechanism of automatic control operates.  ...  In the Plus-Minus game, the task displayed needs to be modeled with the blocks of tens and ones.  ... 
doi:10.1007/978-3-642-30950-2_50 fatcat:be43holne5evfptdogm5rynug4

DeepStealth: Leveraging Deep Learning Models for Stealth Assessment in Game-Based Learning Environments [chapter]

Wookhee Min, Megan H. Frankosky, Bradford W. Mott, Jonathan P. Rowe, Eric Wiebe, Kristy Elizabeth Boyer, James C. Lester
2015 Lecture Notes in Computer Science  
(Left) A lift device with an existing program, and (Right) the programming interface displaying the lift's program.  ...  The findings suggest that deep learning shows considerable promise for automatically inducing stealth assessment models for intelligent game-based learning environments. Figure 1.  ...  Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1007/978-3-319-19773-9_28 fatcat:ovzozm22lbdglbjmdt3laaev4a

Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding

Robert J. Tempelman
2015 Journal of Agricultural Biological and Environmental Statistics  
We focus primarily on mixed model and hierarchical Bayesian analyses which have been most commonly pursued by animal and plant breeders for WGP thus far.  ...  Whole genome prediction (WGP) modeling and genome-wide association (GWA) analyses are big data issues in agricultural quantitative genetics.  ...  Of course, hierarchical modeling inference, whether based on classical mixed models or Bayesian analyses, is now pervasively used in many areas of agricultural, biological, and environmental statistics  ... 
doi:10.1007/s13253-015-0225-2 fatcat:pcqlugugdfd3tlfmwsrf35775u

Modelling and Optimizing Mathematics Learning in Children

Tanja Käser, Alberto Giovanni Busetto, Barbara Solenthaler, Gian-Marco Baschera, Juliane Kohn, Karin Kucian, Michael von Aster, Markus Gross
2013 International Journal of Artificial Intelligence in Education  
This study introduces a student model and control algorithm, optimizing mathematics learning in children.  ...  The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics.  ...  In the PLUS-MINUS game, the task displayed needs to be modelled with the blocks of tens and ones.  ... 
doi:10.1007/s40593-013-0003-7 fatcat:sku2r2a7v5c4njro6njjw2h7jm

Genotype × Environment Interaction for Plant Density Response in Maize ( L.)

Jode W. Edwards
2016 Crop science  
Prior distributions and definitions of parameters in the Bayesian hierarchical model are listed in Table 5 .  ...  Following removal of outliers, a Bayesian hierarchical model was fit in which observed grain yield for an experimental unit in environment i, pedigree j, density l, with inbreeding level k and in incomplete  ... 
doi:10.2135/cropsci2015.07.0408 fatcat:bzqgmpmmxvf6hgmsxv6awdxday

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

Karansher Sandhu, Shruti Sunil Patil, Michael Pumphrey, Arron Carter
2021 The Plant Genome  
This study compares the performance of four machine- and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models.  ...  Overall, this study concluded that machine- and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.  ...  Similarly, machine-and deep-learning-based MT-GS models were compared with their Bayesian and GBLUP counterparts.  ... 
doi:10.1002/tpg2.20119 pmid:34482627 fatcat:4nqmacsylndq3nugoctjocdsza

Addressing scope of inference for global genetic evaluation of livestock

Robert John Tempelman
2010 Revista Brasileira de Zootecnia  
Genetic evaluations should become more accurate with the advent of whole genome selection (WGS) based on high density SNP panels.  ...  conversely, WGS based on estimates, for example, derived from daughter yield deviations of dairy bulls may be too broad to infer upon genetic merit under any one particular environment.  ...  ., , 2007 has previously demonstrated the utility of modeling both genetic and residual variances as multifactorial functions of various effects in hierarchical Bayesian heteroskedastic outlierrobust  ... 
doi:10.1590/s1516-35982010001300029 fatcat:nsnxxqns4rcqtmyafmvevxawg4

Analytics of Business Time Series Using Machine Learning and Bayesian Inference [article]

Bohdan M. Pavlyshenko
2022 arXiv   pre-print
The use of machine learning and Bayesian inference in predictive analytics has been analyzed.  ...  optimization using Q-learning, Bitcoin price modeling, COVID-19 spread impact on stock market, using social networks signals in analytics.  ...  We can also build hierarchical models using Bayesian inference.  ... 
arXiv:2205.12905v2 fatcat:ybcv3jjqljfnpmnjgay5j75s6i

Multi-Trait Machine and Deep Learning Models for Genomic Selection using Spectral Information in a Wheat Breeding Program [article]

Karansher S. Sandhu, Shruti S. Patil, Michael O. Pumphrey, Arron H. Carter
2021 bioRxiv   pre-print
This study compares the performance of four machine and deep learning-based uni-trait (UT) and MT models with traditional GBLUP and Bayesian models.  ...  Four explored Bayesian models gave similar accuracies, which were less than machine and deep learning-based models, and required increased computational time.  ...  Similarly, machine and deep learning-based MT-GS models were 377 compared with their Bayesian and GBLUP counterparts.  ... 
doi:10.1101/2021.04.12.439532 fatcat:xttrdwszh5aplouuakdbmkagdi

Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments

David Kaplan, Chansoon Lee
2018 Evaluation review  
We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach  ...  The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves.  ...  Original path model based on the Program for International Student Assessment data for Case Study 3. Table 1 . 1 Selected Models by Bayesian Model Averaging: Regression Model Using PISA 2009.  ... 
doi:10.1177/0193841x18761421 pmid:29642717 fatcat:bn43z67yprb4hewf3r3lj2ycba

Integrating AI and optimization for decision support: a survey

Amitava Dutta
1996 Decision Support Systems  
Model-based reasoning (MBR) includes Bayesian probability and various linear and nonlinear programming methods, but only the Bayesian methods deal with uncertainty.  ...  Inferencing comes in two main categories: cognitive and model based. Cognitive methods involve knowledge formats and reasoning paradigms.  ...  Feedback from the Program Manager Our team has been in contact with Dr.  ... 
doi:10.1016/s0167-9236(96)80001-7 fatcat:5wl6ev5vvzbonblrldayscjcte

Integrating AI and optimization for decision support: A survey

A Dutta
1996 Decision Support Systems  
Model-based reasoning (MBR) includes Bayesian probability and various linear and nonlinear programming methods, but only the Bayesian methods deal with uncertainty.  ...  Inferencing comes in two main categories: cognitive and model based. Cognitive methods involve knowledge formats and reasoning paradigms.  ...  Feedback from the Program Manager Our team has been in contact with Dr.  ... 
doi:10.1016/0167-9236(96)00026-7 fatcat:4sc74x3gi5hpfkyxen6xf3b5am

FACTOR STRUCTURE OF THE WECHSLER INTELLIGENCE SCALES FOR CHILDREN-FOURTH EDITION AMONG REFERRED NATIVE AMERICAN STUDENTS

Selena Nakano, Marley W. Watkins
2013 Psychology in the schools (Print)  
The Native American population is severely underrepresented in empirical test validity research despite being overrepresented in special education programs and at increased risk for psychoeducational evaluation  ...  The structural validity of the Wechsler Intelligence Scale for Children -Fourth Edition (WISC-IV) was investigated with a sample of 176, six-to-sixteen-year-old Native American children referred for a  ...  As with other structural analyses of the WISC-IV, the first-order models with one to three oblique factors were inferior to the four-factor oblique model and the two hierarchical models.  ... 
doi:10.1002/pits.21724 fatcat:euwp7ya6avbjrpxrhrvdssittq

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

Dimitrije Marković, Jan Gläscher, Peter Bossaerts, John O'Doherty, Stefan J. Kiebel, Wolfgang Einhäuser
2015 PLoS Computational Biology  
Hence, the findings provide interesting and novel insights into the computational mechanisms underlying human behavior when making decisions in complex environments.  ...  PLOS Computational Biology | In particular, we designed a new experimental paradigm and derived novel behavioral models to test the hypothesis that attention modulates the formation of beliefs about the  ...  We used the obtained log-evidences to apply the hierarchical Bayesian model selection approach described in [54, 55] .  ... 
doi:10.1371/journal.pcbi.1004558 pmid:26495984 pmcid:PMC4619749 fatcat:piz7p2ktc5felaow5cljt4q5jq
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