scholarly journals A Bayesian Approach for Timing the Neolithization in Mediterranean Iberia

Radiocarbon ◽  
2017 ◽  
Vol 60 (1) ◽  
pp. 181-205 ◽  
Author(s):  
Oreto García-Puchol ◽  
Joan Bernabeu-Aubán ◽  
C Michael Barton ◽  
Salvador Pardo-Gordó ◽  
Sarah B McClure ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
General Model ◽  
Mixed Model ◽  
Dual Model ◽  
Regional Models ◽  
Neolithic Transition ◽  
Middle Holocene ◽  
Ebro Valley ◽  
Long Period ◽  
Neolithic Expansion

AbstractIn this paper, we compile recent14C dates related to the Neolithic transition in Mediterranean Iberia and present a Bayesian chronological approach for testing thedual model, a mixed model proposed to explain the spread of farming and husbandry processes in eastern Iberia. The dual model postulates the coexistence of agricultural pioneers and indigenous Mesolithic foraging groups in the Middle Holocene. We test this general model with more regional models of four geographical areas (Northeast, Upper, and Middle Ebro Valley, and Eastern and South/Southeastern regions) and present a filtered summed probability of all14C dates known in the region in order to compare socioecological dynamics over a long period. Finally, we discuss the results and analyze how certain specific characteristics of sites and their chronologies can serve for timing the Neolithic expansion in Mediterranean Iberia.

Pronouns

2019 ◽  
pp. 451-473
Author(s):  
Hannah Rohde
Keyword(s):  
Computational Linguistic ◽  
Bayesian Approach ◽  
Real World ◽  
General Model ◽  
Structural Elements ◽  
World Knowledge ◽  
Thematic Roles ◽  
Intended Meaning ◽  
Coherence Relations ◽  
Message Production

When do speakers produce ambiguous expressions? How do comprehenders interpret such expressions to infer a speaker’s intended meaning? This chapter reviews a body of work on pronoun production and interpretation, considering a number of computational, linguistic, and psycholinguistic frameworks and the factors that have been posited to drive pronoun use. These factors include surface structural elements (grammatical role, syntactic parallelism), information structural cues (topichood), lexical semantics and real-world knowledge (thematic roles, causality), as well as aspects of the larger discourse (recency, rhetorical structure, coherence relations). The chapter then turns to a more general model of message production and interpretation that incorporates elements of existing pronoun models using a Bayesian approach.

scholarly journals Daily temperature changes and variability in ENSEMBLES regional models predictions: Evaluation and intercomparison for the Ebro Valley (NE Iberia)

2015 ◽  
Vol 155 ◽  
pp. 141-157 ◽  
Author(s):  
A. El Kenawy ◽  
J.I. López-Moreno ◽  
M.F. McCabe ◽  
N.A. Brunsell ◽  
S.M. Vicente-Serrano
Keyword(s):  
Daily Temperature ◽  
Regional Models ◽  
Temperature Changes ◽  
Ebro Valley

scholarly journals Mixed Model Studies on Inheritance of Reproductive Traits in Laying Hens – A Bayesian Approach

2008 ◽  
Vol 87 (5) ◽  
pp. 878-884 ◽  
Author(s):  
E. Skotarczak ◽  
T. Szwaczkowski ◽  
K. Moliński ◽  
A. Dobek
Keyword(s):  
Bayesian Approach ◽  
Mixed Model ◽  
Laying Hens ◽  
Reproductive Traits ◽  
Model Studies

scholarly journals Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean

2018 ◽  
Vol 53 (10) ◽  
pp. 1093-1100
Author(s):  
Alysson Jalles da Silva ◽  
Adhemar Sanches ◽  
Andréa Carla Bastos Andrade ◽  
Gustavo Hugo Ferreira de Oliveira ◽  
Antonio Orlando Di Mauro
Keyword(s):  
Grain Yield ◽  
Bayesian Approach ◽  
Genetic Parameters ◽  
Mixed Model ◽  
Block Design ◽  
Genetic Parameter ◽  
Uniform Distributions ◽  
Randomized Complete Block Design ◽  
Parameter Values ◽  
The Bayesian Approach

Abstract: The objective of this work was to compare the Bayesian approach and the frequentist methods to estimate means and genetic parameters in soybean multienvironment trials. Fifty-one soybean lines and four controls were evaluated in a randomized complete block design, in six environments, with three replicates, and soybean grain yield was determined. The half-normal prior and uniform distributions were used in combination with parameters obtained from data of 18 genotypes collected in previous and related experiments. The genotypic values of the genotypes of high- and low-grain yield, clustered by the Bayesian approach, differed from the means obtained by the frequentist inference. Soybean assessed through the Bayesian approach showed genetic parameter values of the mixed model (REML/Blup) close to those of the following variables: mean heritability (h2mg), accuracy of genotype selection (Acgen), coefficient of genetic variation (CVgi%), and coefficient of environmental variation (CVe%). Therefore, the mixed model methodology and the Bayesian approach lead to similar results for genetic parameters in multienvironment trials.

scholarly journals Correction to: Bayesian approach to investigate a two-state mixed model of COPD exacerbations

2019 ◽  
Vol 46 (6) ◽  
pp. 627-627
Author(s):  
Anna Largajolli ◽  
Misba Beerahee ◽  
Shuying Yang
Keyword(s):  
Open Access ◽  
Bayesian Approach ◽  
Mixed Model ◽  
Internet Portal ◽  
Creative Commons ◽  
Copd Exacerbations ◽  
Link Type

The article [Bayesian approach to investigate a two-state mixed model of COPD exacerbations], written by [Anna Largajolli, Misba Beerahee, Shuying Yang], was originally published electronically on the publisher’s internet portal (currently SpringerLink) on [13 June 2019] without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on [November 2019] to © The Author(s) [2019] and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

scholarly journals Small Area Estimation on Zero-Inflated Data Using Frequentist and Bayesian Approach

2020 ◽  
Vol 18 (1) ◽  
pp. 2-22
Author(s):  
Kusman Sadik ◽  
Rahma Anisa ◽  
Euis Aqmaliyah
Keyword(s):  
Bayesian Approach ◽  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Small Area Estimation ◽  
Prediction Method ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Best Linear Unbiased ◽  
Sr Method

The most commonly used method of small area estimation (SAE) is the empirical best linear unbiased prediction method based on a linear mixed model. However, it is not appropriate in the case of the zero-inflated target variable with a mixture of zeros and continuously distributed positive values. Therefore, various model-based SAE methods for zero-inflated data are developed, such as the Frequentist approach and the Bayesian approach. Both approaches are compared with the survey regression (SR) method which ignores the presence of zero-inflation in the data. The results show that the two SAE approaches for zero-inflated data are capable to yield more accurate area mean estimates than the SR method.

A Bayesian approach for some zero-modified Poisson mixture models

2019 ◽  
Vol 20 (5) ◽  
pp. 467-501
Author(s):  
Wesley Bertoli ◽  
Katiane S Conceição ◽  
Marinho G Andrade ◽  
Francisco Louzada
Keyword(s):  
Mixture Models ◽  
Bayesian Approach ◽  
Count Data ◽  
General Model ◽  
Model Comparison ◽  
Discrete Distributions ◽  
Simulation Studies ◽  
Relevant Feature ◽  
Poisson Mixture ◽  
Fully Bayesian

In this article, we propose a class of zero-modified Poisson mixture models as an alternative to model overdispersed count data exhibiting inflation or deflation of zeros. A relevant feature of this class is that the zero modification can be incorporated using a zero truncation process and consequently, the proposed models can be expressed in the hurdle version. This procedure leads to the fact that the proposed models can be fitted without any previous information about the zero modification present in agiven dataset. A fully Bayesian approach has been considered for estimation and inference concerns. Three different simulation studies have been conducted to illustrate the performance of the developed methodology. The usefulness of the proposed class of models has been assessed by using three real datasets provided by the literature. A general model comparison with some well-known discrete distributions has been presented.

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