scholarly journals Approximate Bayesian Computation of radiocarbon and paleoenvironmental record shows population resilience on Rapa Nui (Easter Island)

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Robert J. DiNapoli ◽  
Enrico R. Crema ◽  
Carl P. Lipo ◽  
Timothy M. Rieth ◽  
Terry L. Hunt
Keyword(s):  
Approximate Bayesian Computation ◽  
Climatic Changes ◽  
Easter Island ◽  
Human Populations ◽  
Bayesian Computation ◽  
Rapa Nui ◽  
Relative Population ◽  
Radiocarbon Dates ◽  
Approximate Bayesian ◽  
The Impact

AbstractExamining how past human populations responded to environmental and climatic changes is a central focus of the historical sciences. The use of summed probability distributions (SPD) of radiocarbon dates as a proxy for estimating relative population sizes provides a widely applicable method in this research area. Paleodemographic reconstructions and modeling with SPDs, however, are stymied by a lack of accepted methods for model fitting, tools for assessing the demographic impact of environmental or climatic variables, and a means for formal multi-model comparison. These deficiencies severely limit our ability to reliably resolve crucial questions of past human-environment interactions. We propose a solution using Approximate Bayesian Computation (ABC) to fit complex demographic models to observed SPDs. Using a case study from Rapa Nui (Easter Island), a location that has long been the focus of debate regarding the impact of environmental and climatic changes on its human population, we find that past populations were resilient to environmental and climatic challenges. Our findings support a growing body of evidence showing stable and sustainable communities on the island. The ABC framework offers a novel approach for exploring regions and time periods where questions of climate-induced demographic and cultural change remain unresolved.

scholarly journals Modeling of Flowering Time in Vigna radiata with Approximate Bayesian Computation

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2317
Author(s):  
Andrey Ageev ◽  
Cheng-Ruei Lee ◽  
Chau-Ti Ting ◽  
Roland Schafleitner ◽  
Eric Bishop-von Bishop-von Wettberg ◽  
...  
Keyword(s):  
Flowering Time ◽  
Approximate Bayesian Computation ◽  
Climatic Factors ◽  
Permutation Test ◽  
Phenotypic Diversity ◽  
Pearson Correlation ◽  
Day Length ◽  
Bayesian Computation ◽  
Approximate Bayesian ◽  
The Impact

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. A new approach is proposed that uses Approximate Bayesian Computation with Differential Evolution to construct a pool of models for flowering time. The functions for daily progression of the plant from planting to flowering are obtained in analytic form and depend on daily values of climatic factors and genetic information. The resulting pool of models demonstrated high accuracy on the dataset. Day length, solar radiation and temperature had a large impact on the model accuracy, while the impact of precipitation was comparatively small and the impact of maximal temperature has the maximal variation. The model pool was used to investigate the behavior of accessions from the dataset in case of temperature increase by 0.05–6.00∘. The time to flowering changed differently for different accessions. The Pearson correlation coefficient between the SNP value and the change in time to flowering revealed weak but significant association of SNP7 with behavior of the accessions in warming climate conditions. The same SNP was found to have a considerable influence on model prediction with a permutation test. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.

scholarly journals The spread of agriculture in Iberia through Approximate Bayesian Computation and Neolithic projectile tools

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261813
Author(s):  
Alfredo Cortell-Nicolau ◽  
Oreto García-Puchol ◽  
María Barrera-Cruz ◽  
Daniel García-Rivero
Keyword(s):  
Iberian Peninsula ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Cultural Distance ◽  
Geographic Distance ◽  
Distance Matrix ◽  
Mantel Test ◽  
Bayesian Computation ◽  
Radiocarbon Dates ◽  
Approximate Bayesian

In the present article we use geometric microliths (a specific type of arrowhead) and Approximate Bayesian Computation (ABC) in order to evaluate possible origin points and expansion routes for the Neolithic in the Iberian Peninsula. In order to do so, we divide the Iberian Peninsula in four areas (Ebro river, Catalan shores, Xúquer river and Guadalquivir river) and we sample the geometric microliths existing in the sites with the oldest radiocarbon dates for each zone. On this data, we perform a partial Mantel test with three matrices: geographic distance matrix, cultural distance matrix and chronological distance matrix. After this is done, we simulate a series of partial Mantel tests where we alter the chronological matrix by using an expansion model with randomised origin points, and using the distribution of the observed partial Mantel test’s results as a summary statistic within an Approximate Bayesian Computation-Sequential Monte-Carlo (ABC-SMC) algorithm framework. Our results point clearly to a Neolithic expansion route following the Northern Mediterranean, whilst the Southern Mediterranean route could also find support and should be further discussed. The most probable origin points focus on the Xúquer river area.

scholarly journals Interpreting scratch assays using pair density dynamics and approximate Bayesian computation

Open Biology ◽  
2014 ◽  
Vol 4 (9) ◽  
pp. 140097 ◽  
Author(s):  
Stuart T. Johnston ◽  
Matthew J. Simpson ◽  
D. L. Sean McElwain ◽  
Benjamin J. Binder ◽  
Joshua V. Ross
Keyword(s):  
Approximate Bayesian Computation ◽  
Chronic Wounds ◽  
Essential Element ◽  
Cell Spreading ◽  
Bayesian Computation ◽  
Potential Treatment ◽  
Pair Density ◽  
Approximate Bayesian ◽  
The Impact ◽  
Insight Into

Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer. Scratch assays are a technically simple and inexpensive method used to study collective cell spreading; however, most previous interpretations of scratch assays are qualitative and do not provide estimates of the cell diffusivity, D , or the cell proliferation rate, λ . Estimating D and λ is important for investigating the efficacy of a potential treatment and provides insight into the mechanism through which the potential treatment acts. While a few methods for estimating D and λ have been proposed, these previous methods lead to point estimates of D and λ , and provide no insight into the uncertainty in these estimates. Here, we compare various types of information that can be extracted from images of a scratch assay, and quantify D and λ using discrete computational simulations and approximate Bayesian computation. We show that it is possible to robustly recover estimates of D and λ from synthetic data, as well as a new set of experimental data. For the first time, our approach also provides a method to estimate the uncertainty in our estimates of D and λ . We anticipate that our approach can be generalized to deal with more realistic experimental scenarios in which we are interested in estimating D and λ , as well as additional relevant parameters such as the strength of cell-to-cell adhesion or the strength of cell-to-substrate adhesion.

scholarly journals Approximate Bayesian Computation Algorithms for Estimating Network Model Parameters

2017 ◽  
Author(s):  
Ye Zheng ◽  
Stéphane Aris-Brosou
Keyword(s):  
Monte Carlo ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Model Parameters ◽  
Human Populations ◽  
Bayesian Computation ◽  
Transmission Networks ◽  
General Network ◽  
Approximate Bayesian

AbstractStudies on Approximate Bayesian Computation (ABC) replacing the intractable likelihood function in evaluation of the posterior distribution have been developed for several years. However, their field of application has to date essentially been limited to inference in population genetics. Here, we propose to extend this approach to estimating the structure of transmission networks of viruses in human populations. In particular, we are interested in estimating the transmission parameters under four very general network structures: random, Watts-Strogatz, Barabasi-Albert and an extension that incorporates aging. Estimation was evaluated under three approaches, based on ABC, ABC-Markov chain Monte Carlo (ABC-MCMC) and ABC-Sequential Monte Carlo (ABC-SMC) samplers. We show that ABC-SMC samplers outperform both ABC and ABC-MCMC, achieving high accuracy and low variance in simulations. This approach paves the way to estimating parameters of real transmission networks of transmissible diseases.

scholarly journals Weighted approximate Bayesian computation via Sanov’s theorem

2021 ◽  
Author(s):  
Cecilia Viscardi ◽  
Michele Boreale ◽  
Fabio Corradi
Keyword(s):  
Large Deviations ◽  
Posterior Distribution ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Information Theoretic ◽  
Discrete Random Variables ◽  
Positive Weights ◽  
Approximate Bayesian ◽  
Information Theoretic Method ◽  
Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

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