Multitrait and multiharvest analyses for genetic assessment and selection of Tahiti acid lime genotypes through Bayesian inference

Training for policy analysis practice has evolved over forty years to a standardized core, including economics, statistics, management, politics/political science, and a practicum. The original model applied disciplinary methodology to the selection of better alternatives among possible policies for governments and nonprofit organizations. The most important mid-course correction in MPP history was the introduction of public management requirements in recognition that MPP alumni would (i) manage ‘policy shops’ and operating agencies as their careers advanced, and (ii) should advise on policy with awareness of implementability and manageability issues. Variations on this model include courses in law and public administration, concentrations in issue areas like health or environmental policy, and joint degrees with other professional schools. Current issues from which future evolution of the MPP enterprise is likely to flow include tensions between methodologies used by MPP faculty in research and inclusion of models like Bayesian inference and behavioral economics that may be more applicable in professional practice. Another source of variation is pedagogical: some courses offer the familiar ‘Theory T [for telling]’ model whereby content is presented didactically in lectures with discussion assigned to sections, while others move to ‘Theory C [for coaching]’ convention where content presentation is left to readings, and meetings are devoted to using the content to analyze policy questions.

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Genetic parameters and selection of maize cultivars using Bayesian inference in a multi-trait linear model

Acta Agriculturae Scandinavica Section B - Soil & Plant Science ◽

10.1080/09064710.2019.1601764 ◽

2019 ◽

Vol 69 (6) ◽

pp. 465-478

Author(s):

Jan Bocianowski ◽

Kamila Nowosad ◽

Piotr Szulc ◽

Anna Tratwal ◽

Ewa Bakinowska ◽

...

Keyword(s):

Bayesian Inference ◽

Linear Model ◽

Genetic Parameters ◽

Maize Cultivars ◽

Selection Of

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Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference

Journal of Sound and Vibration ◽

10.1016/j.jsv.2021.116418 ◽

2021 ◽

pp. 116418

Author(s):

Philippe Bisaillon ◽

Rimple Sandhu ◽

Chris Pettit ◽

Mohammad Khalil ◽

Dominique Poirel ◽

...

Keyword(s):

Bayesian Inference ◽

Dynamic Model ◽

Modeling Error ◽

Combined Selection ◽

Selection Of

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Interrelations between productive, vegetative, and fruit quality traits in Tahiti acid lime through Bayesian inference

Ciência Rural ◽

10.1590/0103-8478cr20210007 ◽

2022 ◽

Vol 52 (5) ◽

Author(s):

Renan Garcia Malikouski ◽

Emanuel Ferrari do Nascimento ◽

Andréia Lopes de Morais ◽

Marco Antônio Peixoto ◽

Moises Zucoloto ◽

...

Keyword(s):

Bayesian Inference ◽

Fruit Quality ◽

Deviance Information Criterion ◽

Fruit Yield ◽

Soluble Solids ◽

Quality Traits ◽

Breeding Programs ◽

Canopy Volume ◽

Fruit Quality Traits ◽

Acid Lime

ABSTRACT: Although the fruit yield has a core importance in Tahiti acid lime breeding programs, other traits stand out among the quality fruit and vegetative traits as ones that still need to be improved in selection of superior genotypes. Appling efficient tools aiming selection, such as the Bayesian inference, becomes an alternative in perennial crops. This study applied Bayesian inference in the genetic evaluation of Tahiti acid lime genotypes and estimated the interrelation between vegetative, productive and fruit quality traits. Twenty-four acid lime genotypes were evaluated for number of fruits, fruit yield, canopy volume, stem diameter, soluble solids content, shell thickness, and juice yield traits. The genotypic values were estimated through Bayesian inference and models with different residual structure were tested via deviance information criterion. Pearson’s correlation and the path analysis were estimated, removing the multicollinearity effect. The Bayesian inference estimates genotypic values with high selective accuracy. The correlations obtained between traits from different groups can be useful in selection strategies for improvement of Tahiti acid lime. The Bayesian inference demonstrated to be an important tool and should be considered in perennial breeding programs.

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Selection of KL Neighbourhood in Robust Bayesian Inference

Statistical Science ◽

10.1214/16-sts562 ◽

2016 ◽

Vol 31 (4) ◽

pp. 499-502

Author(s):

Natalia A. Bochkina

Keyword(s):

Bayesian Inference ◽

Robust Bayesian Inference ◽

Selection Of

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Improving Learning-from-Crowds through Expert Validation

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/324 ◽

2017 ◽

Cited By ~ 8

Author(s):

Mengchen Liu ◽

Liu Jiang ◽

Junlin Liu ◽

Xiting Wang ◽

Jun Zhu ◽

...

Keyword(s):

Bayesian Inference ◽

Supervised Learning ◽

Real World ◽

Learning Algorithm ◽

State Of The Art ◽

Uncertainty Assessment ◽

Effective Learning ◽

Complete Uncertainty ◽

Expert Validation ◽

Selection Of

Although several effective learning-from-crowd methods have been developed to infer correct labels from noisy crowdsourced labels, a method for post-processed expert validation is still needed. This paper introduces a semi-supervised learning algorithm that is capable of selecting the most informative instances and maximizing the influence of expert labels. Specifically, we have developed a complete uncertainty assessment to facilitate the selection of the most informative instances. The expert labels are then propagated to similar instances via regularized Bayesian inference. Experiments on both real-world and simulated datasets indicate that given a specific accuracy goal (e.g., 95%) our method reduces expert effort from 39% to 60% compared with the state-of-the-art method.

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