Reconciling Simulations and Experiments With BICePs: A Review

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.661520 ◽

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.

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Bayesian Inference and Testing Any Hypothesis You Can Specify

Advances in Methods and Practices in Psychological Science ◽

10.1177/2515245918773087 ◽

2018 ◽

Vol 1 (2) ◽

pp. 281-295 ◽

Cited By ~ 6

Author(s):

Alexander Etz ◽

Julia M. Haaf ◽

Jeffrey N. Rouder ◽

Joachim Vandekerckhove

Keyword(s):

Bayesian Inference ◽

Model Selection ◽

Hypothesis Testing ◽

Likelihood Ratio ◽

Special Form ◽

Null Hypothesis ◽

Bayes Factor ◽

Alternative Hypotheses ◽

Competing Models

Hypothesis testing is a special form of model selection. Once a pair of competing models is fully defined, their definition immediately leads to a measure of how strongly each model supports the data. The ratio of their support is often called the likelihood ratio or the Bayes factor. Critical in the model-selection endeavor is the specification of the models. In the case of hypothesis testing, it is of the greatest importance that the researcher specify exactly what is meant by a “null” hypothesis as well as the alternative to which it is contrasted, and that these are suitable instantiations of theoretical positions. Here, we provide an overview of different instantiations of null and alternative hypotheses that can be useful in practice, but in all cases the inferential procedure is based on the same underlying method of likelihood comparison. An associated app can be found at https://osf.io/mvp53/ . This article is the work of the authors and is reformatted from the original, which was published under a CC-By Attribution 4.0 International license and is available at https://psyarxiv.com/wmf3r/ .

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Bayesian Model Selection in Fisheries Management and Ecology

Journal of Fish and Wildlife Management ◽

10.3996/042019-jfwm-024 ◽

2019 ◽

Vol 10 (2) ◽

pp. 691-707

Author(s):

Jason C. Doll ◽

Stephen J. Jacquemin

Keyword(s):

Bayesian Inference ◽

Model Selection ◽

Fisheries Management ◽

Bayesian Model ◽

Bayes Factor ◽

Evaluation Process ◽

Information Criterion ◽

Bayesian Model Selection ◽

Model Parameters ◽

Inference Methods

Abstract Researchers often test ecological hypotheses relating to a myriad of questions ranging from assemblage structure, population dynamics, demography, abundance, growth rate, and more using mathematical models that explain trends in data. To aid in the evaluation process when faced with competing hypotheses, we employ statistical methods to evaluate the validity of these multiple hypotheses with the goal of deriving the most robust conclusions possible. In fisheries management and ecology, frequentist methodologies have largely dominated this approach. However, in recent years, researchers have increasingly used Bayesian inference methods to estimate model parameters. Our aim with this perspective is to provide the practicing fisheries ecologist with an accessible introduction to Bayesian model selection. Here we discuss Bayesian inference methods for model selection in the context of fisheries management and ecology with empirical examples to guide researchers in the use of these methods. In this perspective we discuss three methods for selecting among competing models. For comparing two models we discuss Bayes factor and for more complex models we discuss Watanabe–Akaike information criterion and leave-one-out cross-validation. We also describe what kinds of information to report when conducting Bayesian inference. We conclude this review with a discussion of final thoughts about these model selection techniques.

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Bayesian inference and testing any hypothesis you can specify

10.31234/osf.io/wmf3r ◽

2018 ◽

Cited By ~ 1

Author(s):

Alexander Etz ◽

Julia M. Haaf ◽

Jeffrey N. Rouder ◽

Joachim Vandekerckhove

Keyword(s):

Bayesian Inference ◽

Model Selection ◽

Hypothesis Testing ◽

Likelihood Ratio ◽

Special Form ◽

Null Hypothesis ◽

Bayes Factor ◽

Alternative Hypotheses ◽

Competing Models

Hypothesis testing is a special form of model selection. Once a pair of competing models is fully defined, their definition immediately leads to a measure of how strongly each model supports the data. The ratio of their support is often called the likelihood ratio or the Bayes factor. Critical in the model selection endeavor is the specification of the models. In the case of hypothesis testing, it is of the greatest importance that we specify exactly what is meant by a "null" hypothesis as well as the alternative to which it is contrasted, and that these are suitable instantiations of theoretical positions. Here, we provide an overview of different instantiations of null and alternative hypotheses that can be useful in practice, while the underlying method of likelihood comparison is universal and identical in all cases. An associated app can be found via https://osf.io/mvp53/.

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PathFinder: Bayesian inference of clone migration histories in cancer

Bioinformatics ◽

10.1093/bioinformatics/btaa795 ◽

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.

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Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for Rt Estimation

10.21203/rs.3.rs-137557/v1 ◽

2021 ◽

Author(s):

Xian Yang ◽

Shuo Wang ◽

Yuting Xing ◽

Ling Li ◽

Richard Yi Da Xu ◽

...

Keyword(s):

Bayesian Inference ◽

Model Selection ◽

Reproduction Number ◽

State Of The Art ◽

Transmission Dynamics ◽

Selection Mechanism ◽

Problem Of Time ◽

Joint Inference ◽

Abrupt Changes ◽

Bayesian Smoothing

Abstract In epidemiological modelling, the instantaneous reproduction number, Rt, is important to understand the transmission dynamics of infectious diseases. Current Rt estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of Rt. To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for Rt estimation, resulting in the state-of-the-art ‘DARt’ system for Rt estimation. With DARt, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.

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An In-Class Demonstration of Bayesian Inference

10.31234/osf.io/d8bvn ◽

2019 ◽

Author(s):

Johnny van Doorn ◽

Dora Matzke ◽

Eric-Jan Wagenmakers

Keyword(s):

Bayesian Inference ◽

Posterior Distribution ◽

Prior Distribution ◽

Sequential Analysis ◽

Bayes Factor ◽

Classroom Setting ◽

Key Concepts

Sir Ronald Fisher's venerable experiment "The Lady Tasting Tea'' is revisited from a Bayesian perspective. We demonstrate how a similar tasting experiment, conducted in a classroom setting, can familiarize students with several key concepts of Bayesian inference, such as the prior distribution, the posterior distribution, the Bayes factor, and sequential analysis.

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Open Universities Australia

Global Challenges and Perspectives in Blended and Distance Learning ◽

10.4018/978-1-4666-3978-2.ch006 ◽

2013 ◽

pp. 83-98

Author(s):

Michael Crock ◽

Janet Baker ◽

Skye Turner-Walker

Keyword(s):

Higher Education ◽

Open Access ◽

Federal Government ◽

Technological Innovation ◽

Teaching And Learning ◽

Student Loans ◽

Future Directions ◽

History Of ◽

Insight Into ◽

Future Plans

This chapter analyses the history of, and future directions for, higher education studies undertaken through Open Universities Australia (OUA), Australia’s unique higher education conduit. Founded to provide open access to units that allow individuals to undertake individual units or achieve qualifications from leading Australian universities, and supported by a federal government student loans scheme, OUA’s experience and future plans provide significant insight into the potential and pitfalls of the technological innovation in both higher education distance, and increasingly, on-campus, teaching and learning. The need for an ongoing emphasis on innovation, adaptability, and cooperation in an extraordinarily rapidly changing environment is highlighted.

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Open Universities Australia

Open Source Technology ◽

10.4018/978-1-4666-7230-7.ch019 ◽

2015 ◽

pp. 320-335

Author(s):

Michael Crock ◽

Janet Baker ◽

Skye Turner-Walker

Keyword(s):

Higher Education ◽

Open Access ◽

Federal Government ◽

Technological Innovation ◽

Teaching And Learning ◽

Student Loans ◽

Future Directions ◽

History Of ◽

Insight Into ◽

Future Plans

This chapter analyses the history of, and future directions for, higher education studies undertaken through Open Universities Australia (OUA), Australia's unique higher education conduit. Founded to provide open access to units that allow individuals to undertake individual units or achieve qualifications from leading Australian universities, and supported by a federal government student loans scheme, OUA's experience and future plans provide significant insight into the potential and pitfalls of the technological innovation in both higher education distance, and increasingly, on-campus, teaching and learning. The need for an ongoing emphasis on innovation, adaptability, and cooperation in an extraordinarily rapidly changing environment is highlighted.

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B → D*lv at non-zero recoil

EPJ Web of Conferences ◽

10.1051/epjconf/201817513003 ◽

2018 ◽

Vol 175 ◽

pp. 13003 ◽

Cited By ~ 6

Author(s):

Alejandro Vaquero Avilés-Casco ◽

Carleton DeTar ◽

Daping Du ◽

Aida El-Khadra ◽

Andreas Kronfeld ◽

...

Keyword(s):

Matrix Element ◽

Decay Rate ◽

Form Factors ◽

Heavy Quarks ◽

Experimental Measurements ◽

Ckm Matrix ◽

Preliminary Results ◽

Future Plans

We present preliminary results from our analysis of the form factors for the B → D*lv decay at non-zero recoil. Our analysis includes 15 MILC asqtad ensembles with Nf = 2 + 1 flavors of sea quarks and lattice spacings ranging from a ≈ 0.15 fm down to 0.045 fm. The valence light quarks employ the asqtad action, whereas the heavy quarks are treated using the Fermilab action. We conclude with a discussion of future plans and phenomenological implications. When combined with experimental measurements of the decay rate, our calculation will enable a determination of the CKM matrix element |Vcb|.

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