A History of Bayesian Inference in Educational Measurement

2021 ◽  
pp. 292-317
Author(s):  
Roy Levy ◽  
Robert J. Mislevy
Keyword(s):  
Bayesian Inference ◽  
Educational Measurement ◽  
History Of

scholarly journals PathFinder: Bayesian inference of clone migration histories in cancer

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i675-i683
Author(s):  
Sudhir Kumar ◽  
Antonia Chroni ◽  
Koichiro Tamura ◽  
Maxwell Sanderford ◽  
Olumide Oladeinde ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Cancer Cells ◽  
Bayesian Approach ◽  
Genetic Variants ◽  
Cancer Development ◽  
Posterior Probabilities ◽  
Migration Routes ◽  
Primary Tumors ◽  
History Of ◽  
The Web

Abstract Summary Metastases cause a vast majority of cancer morbidity and mortality. Metastatic clones are formed by dispersal of cancer cells to secondary tissues, and are not medically detected or visible until later stages of cancer development. Clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here, we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny, the number of mutational differences among clones, and the information on the presence and absence of observed clones in primary and metastatic tumors. By analyzing simulated datasets, we found that PathFinder performes well in reconstructing clone migrations from the primary tumor to new metastases as well as between metastases. It was more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor, and by increasing the number of genetic variants assayed per clone. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes. In conclusion, we anticipate that the use of PathFinder will enable a more reliable inference of migration histories and their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases. Availability and implementation PathFinder is available on the web at https://github.com/SayakaMiura/PathFinder.

scholarly journals Interspecies variation of larval locomotion kinematics in the genus Drosophila and its relation to habitat temperature

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuji Matsuo ◽  
Akinao Nose ◽  
Hiroshi Kohsaka
Keyword(s):  
Bayesian Inference ◽  
Environmental Factors ◽  
Evolutionary History ◽  
Evolutionary Rate ◽  
Phylogenetic Analyses ◽  
The Body ◽  
Quantitative Relation ◽  
Individual Species ◽  
Habitat Temperature ◽  
History Of

Abstract Background Speed and trajectory of locomotion are the characteristic traits of individual species. Locomotion kinematics may have been shaped during evolution towards increased survival in the habitats of each species. Although kinematics of locomotion is thought to be influenced by habitats, the quantitative relation between the kinematics and environmental factors has not been fully revealed. Here, we performed comparative analyses of larval locomotion in 11 Drosophila species. Results We found that larval locomotion kinematics are divergent among the species. The diversity is not correlated to the body length but is correlated instead to the habitat temperature of the species. Phylogenetic analyses using Bayesian inference suggest that the evolutionary rate of the kinematics is diverse among phylogenetic tree branches. Conclusions The results of this study imply that the kinematics of larval locomotion has diverged in the evolutionary history of the genus Drosophila and evolved under the effects of the ambient temperature of habitats.

scholarly journals Interspecies variation of larval locomotion kinematics in the genus Drosophila and its relation to habitat temperature

2020 ◽  
Author(s):  
Yuji Matsuo ◽  
Akinao Nose ◽  
Hiroshi Kohsaka
Keyword(s):  
Bayesian Inference ◽  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Evolutionary Rate ◽  
Phylogenetic Analyses ◽  
The Body ◽  
Quantitative Relation ◽  
Individual Species ◽  
Habitat Temperature ◽  
History Of

AbstractSpeed and trajectory of locomotion are characteristic traits of individual species. During evolution, locomotion kinematics is likely to have been tuned for survival in the habitats of each species. Although kinematics of locomotion is thought to be influenced by habitats, the quantitative relation between the kinematics and environmental factors has not been fully revealed. Here, we performed comparative analyses of larval locomotion in 11 Drosophila species. We found that larval locomotion kinematics are divergent among the species. The diversity is not correlated to the body length but is correlated instead to the minimum habitat temperature of the species. Phylogenetic analyses using Bayesian inference suggest that the evolutionary rate of the kinematics is diverse among phylogenetic trees. The results of this study imply that the kinematics of larval locomotion has diverged in the evolutionary history of the genus Drosophila and evolved under the effects of the minimum ambient temperature of habitats.

scholarly journals Panchromatic SED fitting codes and modelling techniques

2019 ◽  
Vol 15 (S341) ◽  
pp. 26-34
Author(s):  
Maarten Baes
Keyword(s):  
Bayesian Inference ◽  
Physical Properties ◽  
Evolutionary History ◽  
The Past ◽  
History Of ◽  
Modelling Techniques ◽  
New Codes ◽  
Near Future

AbstractModelling and interpreting the SEDs of galaxies has become one of the key tools at the disposal of extragalactic astronomers. Ideally, we could hope that, through a detailed study of its SED, we can infer the correct physical properties and the evolutionary history of a galaxy. In the past decade, panchromatic SED fitting, i.e. modelling the SED over the entire UV–submm wavelength regime, has seen an enormous advance. Several advanced new codes have been developed, nearly all based on Bayesian inference modelling. In this review, we briefly touch upon the different ingredients necessary for panchromatic SED modelling, and discuss the methodology and some important aspects of Bayesian SED modelling. The current uncertainties and limitations of panchromatic SED modelling are discussed, and we explore some avenues how the models and techniques can potentially be improved in the near future.

scholarly journals Bayesian Inference of the Demographic History of Chimpanzees

2010 ◽  
Vol 27 (6) ◽  
pp. 1425-1435 ◽  
Author(s):  
D. Wegmann ◽  
L. Excoffier
Keyword(s):  
Bayesian Inference ◽  
Demographic History ◽  
History Of

scholarly journals Reconciling Simulations and Experiments With BICePs: A Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Vincent A. Voelz ◽  
Yunhui Ge ◽  
Robert M. Raddi
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Bayes Factor ◽  
Bayesian Framework ◽  
Experimental Measurements ◽  
Processing Step ◽  
History Of ◽  
Future Plans

Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements. The Bayesian framework of BICePs enables population reweighting as a post-simulation processing step, with several advantages over existing methods, including the proper use of reference potentials, and the estimation of a Bayes factor-like quantity called the BICePs score for model selection. Here, we summarize the theory underlying this method in context with related algorithms, review the history of BICePs applications to date, and discuss current shortcomings along with future plans for improvement.

scholarly journals Bayesian analysis of mixture autoregressive models covering the complete parameter space

2021 ◽  
Author(s):  
Davide Ravagli ◽  
Georgi N. Boshnakov
Keyword(s):  
Time Series ◽  
Bayesian Inference ◽  
Parameter Space ◽  
New Method ◽  
A Posteriori ◽  
Additional Advantage ◽  
Density Forecasting ◽  
History Of ◽  
Model Time Series ◽  
And Performance

AbstractMixture autoregressive (MAR) models provide a flexible way to model time series with predictive distributions which depend on the recent history of the process and are able to accommodate asymmetry and multimodality. Bayesian inference for such models offers the additional advantage of incorporating the uncertainty in the estimated models into the predictions. We introduce a new way of sampling from the posterior distribution of the parameters of MAR models which allows for covering the complete parameter space of the models, unlike previous approaches. We also propose a relabelling algorithm to deal a posteriori with label switching. We apply our new method to simulated and real datasets, discuss the accuracy and performance of our new method, as well as its advantages over previous studies. The idea of density forecasting using MCMC output is also introduced.

scholarly journals PathFinder: Bayesian inference of clone migration histories in cancer

2020 ◽  
Author(s):  
Sudhir Kumar ◽  
Antonia Chroni ◽  
Koichiro Tamura ◽  
Maxwell Sanderford ◽  
Olumide Oladeinde ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Cancer Cells ◽  
Bayesian Approach ◽  
Genetic Variants ◽  
Cancer Development ◽  
Posterior Probabilities ◽  
Migration Routes ◽  
Primary Tumors ◽  
History Of ◽  
The Web

AbstractSummaryMetastases form by dispersal of cancer cells to secondary tissues. They cause a vast majority of cancer morbidity and mortality. Metastatic clones are not medically detected or visible until later stages of cancer development. Thus, clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny and the numbers of mutational differences among clones, along with the information on the presence and absence of observed clones in different primary and metastatic tumors. In the analysis of simulated datasets, PathFinder performed well in reconstructing migrations from the primary tumor to new metastases as well as between metastases. However, it was much more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor and by increasing the number of genetic variants assayed. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes.ConclusionsWe anticipate that the use of PathFinder will enable a more reliable inference of migration histories, along with their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases.AvailabilityPathFinder is available on the web at https://github.com/SayakaMiura/[email protected]

BAYESIAN ECONOMETRICS: The First Twenty Years

1996 ◽  
Vol 12 (3) ◽  
pp. 500-516 ◽  
Author(s):  
Duo QIN
Keyword(s):  
Bayesian Inference ◽  
Bayesian Econometrics ◽  
History Of

This paper sketches the history of how Bayesian inference was adopted and utilized in econometrics during its first 20 years. It focuses on the causes of the Bayesian movement, the ways in which Bayesian inference was applied, the problems that the application was intended to solve, and the results achieved. It shows that Bayesian research has largely followed mainstream econometric development as far as the major econometric ideas and methods are concerned and that Bayesian reformulation of mainstream econometrics has nevertheless helped in deepening econometricians' understanding of many modeling problems by presenting them from a different angle.

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