scholarly journals Approximate Bayesian computation in controlled branching processes: the role of summary statistics

2020 ◽  
Vol 114 (3) ◽  
Author(s):  
Miguel González ◽  
Rodrigo Martínez ◽  
Carmen Minuesa ◽  
Inés del Puerto
Keyword(s):  
Approximate Bayesian Computation ◽  
Branching Processes ◽  
Bayesian Computation ◽  
Summary Statistics ◽  
Approximate Bayesian ◽  
Controlled Branching Processes

scholarly journals Approximate Bayesian Computation Reveals the Crucial Role of Oceanic Islands for the Assembly of Continental Biodiversity

2015 ◽  
Vol 64 (4) ◽  
pp. 579-589 ◽  
Author(s):  
Jairo Patiño ◽  
Mark Carine ◽  
Patrick Mardulyn ◽  
Nicolas Devos ◽  
Rubén G. Mateo ◽  
...  
Keyword(s):  
Crucial Role ◽  
Approximate Bayesian Computation ◽  
Oceanic Islands ◽  
Bayesian Computation ◽  
Approximate Bayesian

scholarly journals Choice of Summary Statistic Weights in Approximate Bayesian Computation

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Hsuan Jung ◽  
Paul Marjoram
Keyword(s):  
Genetic Algorithm ◽  
Population Genetics ◽  
Approximate Bayesian Computation ◽  
Fundamental Problem ◽  
Bayesian Computation ◽  
Summary Statistics ◽  
Summary Statistic ◽  
Improved Performance ◽  
Approximate Bayesian ◽  
Computation Analysis

In this paper, we develop a Genetic Algorithm that can address the fundamental problem of how one should weight the summary statistics included in an approximate Bayesian computation analysis built around an accept/reject algorithm, and how one might choose the tolerance for that analysis. We then demonstrate that using weighted statistics, and a well-chosen tolerance, in such an approximate Bayesian computation approach can result in improved performance, when compared to unweighted analyses, using one example drawn purely from statistics and two drawn from the estimation of population genetics parameters.

scholarly journals Choosing summary statistics by least angle regression for approximate Bayesian computation

2016 ◽  
Vol 43 (12) ◽  
pp. 2191-2202 ◽  
Author(s):  
Muhammad Faisal ◽  
Andreas Futschik ◽  
Ijaz Hussain ◽  
Mitwali Abd-el.Moemen
Keyword(s):  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Summary Statistics ◽  
Least Angle Regression ◽  
Approximate Bayesian

scholarly journals Component-wise Approximate Bayesian Computation via Gibbs-like steps

Biometrika ◽  
2020 ◽  
Author(s):  
Grégoire Clarté ◽  
Christian P Robert ◽  
Robin J Ryder ◽  
Julien Stoehr
Keyword(s):  
Markov Chain ◽  
Posterior Distribution ◽  
Approximate Bayesian Computation ◽  
Generative Models ◽  
Bayesian Computation ◽  
Summary Statistics ◽  
Posterior Distributions ◽  
Reduced Dimensions ◽  
Standard Solution ◽  
Approximate Bayesian

Abstract Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty, we explore a Gibbs version of the Approximate Bayesian computation approach that runs component-wise approximate Bayesian computation steps aimed at the corresponding conditional posterior distributions, and based on summary statistics of reduced dimensions. While lacking the standard justifications for the Gibbs sampler, the resulting Markov chain is shown to converge in distribution under some partial independence conditions. The associated stationary distribution can further be shown to be close to the true posterior distribution and some hierarchical versions of the proposed mechanism enjoy a closed form limiting distribution. Experiments also demonstrate the gain in efficiency brought by the Gibbs version over the standard solution.

scholarly journals Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Tom Burr ◽  
Andrea Favalli ◽  
Marcie Lombardi ◽  
Jacob Stinnett
Keyword(s):  
Approximate Bayesian Computation ◽  
Gamma Ray ◽  
Bayesian Computation ◽  
Summary Statistics ◽  
Area Estimation ◽  
Estimation Algorithms ◽  
Gamma Ray Spectroscopy ◽  
Peak Location ◽  
Approximate Bayesian ◽  
Energy Channels

Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many RIID algorithms rely on locating peaks and estimating each peak’s net area, peak location and peak area estimation algorithms continue to be developed for gamma-ray spectroscopy. This paper shows that approximate Bayesian computation (ABC) can be effective for peak location and area estimation. Algorithms to locate peaks can be applied to raw or smoothed data, and among several smoothing options, the iterative bias reduction algorithm (IBR) is recommended; the use of IBR with ABC is shown to potentially reduce uncertainty in peak location estimation. Extracted peak locations and areas can then be used as summary statistics in a new ABC-based RIID. ABC allows for easy experimentation with candidate summary statistics such as goodness-of-fit scores and peak areas that are extracted from relatively high dimensional gamma spectra with photopeaks (1024 or more energy channels) consisting of count rates versus energy for a large number of gamma energies.

scholarly journals Approximate Bayesian Computation by Modelling Summary Statistics in a Quasi-likelihood Framework

2015 ◽  
Vol 10 (2) ◽  
pp. 411-439 ◽  
Author(s):  
Stefano Cabras ◽  
Maria Eugenia Castellanos Nueda ◽  
Erlis Ruli
Keyword(s):  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Summary Statistics ◽  
Approximate Bayesian
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