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POPE: post optimization posterior evaluation of likelihood free models

Overview of attention for article published in BMC Bioinformatics, August 2015
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3 tweeters

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Title
POPE: post optimization posterior evaluation of likelihood free models
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/s12859-015-0658-1
Pubmed ID
Authors

Edward Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling

Abstract

In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow, constrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is common practice, resulting in a single parameter setting that minimizes an objective subject to constraints. We propose algorithms for post optimization posterior evaluation (POPE) of simulators. The algorithms compute and visualize all simulations that can generate results of the same or better quality than the optimum, subject to constraints. These optimization posteriors are desirable for a number of reasons among which are easy interpretability, automatic parameter sensitivity and correlation analysis, and posterior predictive analysis. Our algorithms are simple extensions to an existing simulation-based inference framework called approximate Bayesian computation. POPE is applied two biological simulators: a fast and stochastic simulator of stem-cell cycling and a slow and deterministic simulator of tumor growth patterns. POPE allows the scientist to explore and understand the role that constraints, both on the input and the output, have on the optimization posterior. As a Bayesian inference procedure, POPE provides a rigorous framework for the analysis of the uncertainty of an optimal simulation parameter setting.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 6%
France 1 6%
Unknown 16 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 56%
Student > Bachelor 2 11%
Lecturer 1 6%
Student > Master 1 6%
Student > Ph. D. Student 1 6%
Other 2 11%
Unknown 1 6%
Readers by discipline Count As %
Computer Science 6 33%
Business, Management and Accounting 3 17%
Mathematics 1 6%
Agricultural and Biological Sciences 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 4 22%
Unknown 2 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 August 2015.
All research outputs
#8,687,836
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#3,315
of 4,195 outputs
Outputs of similar age
#148,750
of 235,749 outputs
Outputs of similar age from BMC Bioinformatics
#95
of 128 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 128 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.