Browsing by Subject "approximate Bayesian computation"

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  • Siren, Jukka; Lens, Luc; Cousseau, Laurence; Ovaskainen, Otso (2018)
    1. Individual-based models (IBMs) allow realistic and flexible modelling of ecological systems, but their parameterization with empirical data is statistically and computationally challenging. Approximate Bayesian computation (ABC) has been proposed as an efficient approach for inference with IBMs, but its applicability to data on natural populations has not been yet fully explored. 2. We construct an IBM for the metapopulation dynamics of a species inhabiting a fragmented patch network, and develop an ABC method for parameterization of the model. We consider several scenarios of data availability from count data to combination of mark-recapture and genetic data. We analyse both simulated and real data on white-starred robin (Pogonocichla stellata), a passerine bird living in montane forest environment in Kenya, and assess how the amount and type of data affect the estimates of model parameters and indicators of population state. 3. The indicators of the population state could be reliably estimated using the ABC method, but full parameterization was not achieved due to strong posterior correlations between model parameters. While the combination of the data types did not provide more accurate estimates for most of the indicators of population state or model parameters than the most informative data type (ringing data or genetic data) alone, the combined data allowed robust simultaneous estimation of all unknown quantities. 4. Our results show that ABC methods provide a powerful and flexible technique forparameterizing complex IBMs with multiple data sources, and assessing the dynamics of the population in a robust manner.
  • Gutmann, Michael U.; Corander, Jukka (2016)
    Our paper deals with inferring simulator-based statistical models given some observed data. A simulator-based model is a parametrized mechanism which specifies how data are generated. It is thus also referred to as generative model. We assume that only a finite number of parameters are of interest and allow the generative process to be very general; it may be a noisy nonlinear dynamical system with an unrestricted number of hidden variables. This weak assumption is useful for devising realistic models but it renders statistical inference very difficult. The main challenge is the intractability of the likelihood function. Several likelihood-free inference methods have been proposed which share the basic idea of identifying the parameters by finding values for which the discrepancy between simulated and observed data is small. A major obstacle to using these methods is their computational cost. The cost is largely due to the need to repeatedly simulate data sets and the lack of knowledge about how the parameters affect the discrepancy. We propose a strategy which combines probabilistic modeling of the discrepancy with optimization to facilitate likelihood-free inference. The strategy is implemented using Bayesian optimization and is shown to accelerate the inference through a reduction in the number of required simulations by several orders of magnitude.
  • Lintusaari, Jarno; Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka (2017)
    Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments.
  • Gargiulo, Roberta; Pironon, Samuel; Zheleznaya, Ekaterina; Sanchez, Michele D.; Balazs, Zoltan R.; Podar, Dorina; Wilkinson, Timothy; Jäkäläniemi, Anne; Kull, Tiiu; Väre, Henry; Fay, Michael F. (2019)
    Aim We investigated the phylogeographical history of a clonal-sexual orchid, to test the hypothesis that current patterns of genetic diversity and differentiation retain the traces of climatic fluctuations and of the species reproductive system. Location Europe, Siberia and Russian Far East. Taxon Cypripedium calceolus L. (Orchidaceae). Methods Samples (>900, from 56 locations) were genotyped at 11 nuclear microsatellite loci and plastid sequences were obtained for a subset of them. Analysis of genetic structure and approximate Bayesian computations were performed. Species distribution modelling was used to explore the effects of past climatic fluctuations on the species range. Results Analysis of genetic diversity reveals high heterozygosity and allele diversity, with no geographical trend. Three genetic clusters are identified with extant gene pools derived from ancestral demes in glacial refugia. Siberian populations exhibit different plastid haplotypes, supporting an early divergence for the Asian gene pool. Demographic results based on genetic data are compatible with an admixture event explaining differentiation in Estonia and Romania and they are consistent with past climatic dynamics inferred through species distribution modelling. Current population differentiation does not follow isolation by distance model and is compatible with a model of isolation by colonization. Main conclusions The genetic differentiation observed today in C. calceolus preserves the signature of climatic fluctuations in the historical distribution range of the species. Our findings support the central role of clonal reproduction in the reducing loss of diversity through genetic drift. The dynamics of the clonal-sexual reproduction are responsible for the persistence of ancestral variation and stability during glacial periods and post-glacial expansion.