Academic literature on the topic 'Approximate Bayesian Computation'

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Journal articles on the topic "Approximate Bayesian Computation"

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Beaumont, Mark A. "Approximate Bayesian Computation." Annual Review of Statistics and Its Application 6, no. 1 (2019): 379–403. http://dx.doi.org/10.1146/annurev-statistics-030718-105212.

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Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models. This review gives an overview of the method and the main issues and challenges that are the subject of current research.
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Sunnåker, Mikael, Alberto Giovanni Busetto, Elina Numminen, Jukka Corander, Matthieu Foll, and Christophe Dessimoz. "Approximate Bayesian Computation." PLoS Computational Biology 9, no. 1 (2013): e1002803. http://dx.doi.org/10.1371/journal.pcbi.1002803.

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Celeux, Gilles. "Approximate Bayesian computation methods." Statistics and Computing 22, no. 6 (2012): 1165–66. http://dx.doi.org/10.1007/s11222-012-9350-8.

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Beaumont, M. A., J. M. Cornuet, J. M. Marin, and C. P. Robert. "Adaptive approximate Bayesian computation." Biometrika 96, no. 4 (2009): 983–90. http://dx.doi.org/10.1093/biomet/asp052.

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Templeton, Alan R. "Correcting Approximate Bayesian Computation." Trends in Ecology & Evolution 25, no. 9 (2010): 488–89. http://dx.doi.org/10.1016/j.tree.2010.06.009.

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Prescott, Thomas P., and Ruth E. Baker. "Multifidelity Approximate Bayesian Computation." SIAM/ASA Journal on Uncertainty Quantification 8, no. 1 (2020): 114–38. http://dx.doi.org/10.1137/18m1229742.

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Turner, Brandon M., and Trisha Van Zandt. "Hierarchical Approximate Bayesian Computation." Psychometrika 79, no. 2 (2013): 185–209. http://dx.doi.org/10.1007/s11336-013-9381-x.

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Fearnhead, Paul, and Dennis Prangle. "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74, no. 3 (2012): 419–74. http://dx.doi.org/10.1111/j.1467-9868.2011.01010.x.

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Martin, James S., Ajay Jasra, Sumeetpal S. Singh, Nick Whiteley, Pierre Del Moral, and Emma McCoy. "Approximate Bayesian Computation for Smoothing." Stochastic Analysis and Applications 32, no. 3 (2014): 397–420. http://dx.doi.org/10.1080/07362994.2013.879262.

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Franks, Jordan J. "Handbook of Approximate Bayesian Computation." Journal of the American Statistical Association 115, no. 532 (2020): 2100–2101. http://dx.doi.org/10.1080/01621459.2020.1846973.

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Dissertations / Theses on the topic "Approximate Bayesian Computation"

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Ratmann, Oliver Rene. "Approximate Bayesian Computation under model uncertainty." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520934.

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Webster, Mark Graham Moody. "Convergence properties of approximate Bayesian computation." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/16197/.

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Approximate Bayesian Computation is a family of Monte Carlo methods used for likelihood-free Bayesian inference, where calculating the likelihood is intractable, but it is possible to generate simulated data, and calculate summary statistics. While these methods are easy to describe and implement, it is not trivial to optimise the mean square error of the resulting estimate. This thesis focuses on asymptotic results for the rate of convergence of ABC to the true posterior expectation as the expected computational cost increases. Firstly, we examine the asymptotic efficiency of the "basic" versions of ABC, which consists of proposal generation, followed by a simple accept-reject step. We then look at several simple extensions, including the use of a random accept-reject step, and the use of ABC to make kernel density estimates. The asymptotic convergence rate of the basic versions of ABC decreases as the summary statistic dimension increases. A naive conclusion from this result would be that, for an infinite-dimensional summary statistic, the ABC estimate would not converge. To show this need not be the case, we look at the asymptotic behaviour of ABC in the case of an observation that consists of a stochastic process over a fixed time interval. We find partial results for two different criteria for accepting proposals. We also introduce a new variant of ABC, referred to in the thesis as the ABCLOC estimate. This belongs to a family of variants, in which the parameter proposals are adjusted, to reduce the difference between the distribution of the accepted proposals and the true posterior distribution. The ABCLOC estimate does this using kernel regression. We give preliminary results for the asymptotic behaviour of the ABCLOC estimate, showing that it potentially has a faster asymptotic rate of convergence than the basic versions for high-dimensional summary statistics.
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Ruli, Erlis. "Recent Advances in Approximate Bayesian Computation Methods." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423529.

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The Bayesian approach to statistical inference in fundamentally probabilistic. Exploiting the internal consistency of the probability framework, the posterior distribution extracts the relevant information in the data, and provides a complete and coherent summary of post data uncertainty. However, summarising the posterior distribution often requires the calculation of awkward multidimensional integrals. A further complication with the Bayesian approach arises when the likelihood functions is unavailable. In this respect, promising advances have been made by theory of Approximate Bayesian Computations (ABC). This thesis focuses on computational methods for the approximation of posterior distributions, and it discusses six original contributions. The first contribution concerns the approximation of marginal posterior distributions for scalar parameters. By combining higher-order tail area approximation with the inverse transform sampling, we define the HOTA algorithm which draws independent random sample from the approximate marginal posterior. The second discusses the HOTA algorithm with pseudo-posterior distributions, \eg, posterior distributions obtained by the combination of a pseudo-likelihood with a prior within Bayes' rule. The third contribution extends the use of tail-area approximations to contexts with multidimensional parameters, and proposes a method which gives approximate Bayesian credible regions with good sampling coverage properties. The forth presents an improved Laplace approximation which can be used for computing marginal likelihoods. The fifth contribution discusses a model-based procedure for choosing good summary statistics for ABC, by using composite score functions. Lastly, the sixth contribution discusses the choice of a default proposal distribution for ABC that is based on the notion of quasi-likelihood.<br>L'approccio bayesiano all'inferenza statistica è fondamentalmente probabilistico. Attraverso il calcolo delle probabilità, la distribuzione a posteriori estrae l'informazione rilevante offerta dai dati e produce una descrizione completa e coerente dell'incertezza condizionatamente ai dati osservati. Tuttavia, la descrizione della distribuzione a posteriori spesso richiede il computo di integrali multivariati e complicati. Un'ulteriore difficoltà dell'approccio bayesiano è legata alla funzione di verosimiglianza e nasce quando quest'ultima è matematicamento o computazionalmente intrattabile. In questa direzione, notevoli sviluppi sono stati compiuti dalla cosiddetta teaoria di Approximate Bayesian Computations (ABC). Questa tesi si focalizza su metodi computazionali per l'approssimazione della distribuzione a posteriori e propone sei contributi originali. Il primo contributo concerne l'approssimazione della distributione a posteriori marginale per un parametro scalare. Combinando l'approssimazione di ordine superiore per tail-area con il metodo della simulazione per inversione, si ottiene l'algorimo denominato HOTA, il quale può essere usato per simulare in modo indipendente da un'approssimazione della distribuzione a posteriori. Il secondo contributo si propone di estendere l'uso dell'algoritmo HOTA in contesti di distributioni pseudo-posterior, ovvero una distribuzione a posteriori ottenuta attraverso la combinazione di una pseudo-verosimiglianza con una prior, tramite il teorema di Bayes. Il terzo contributo estende l'uso dell'approssimazione di tail-area in contesti con parametri multidimensionali e propone un metodo per calcolare delle regioni di credibilità le quali presentano buone proprietà di copertura frequentista. Il quarto contributo presenta un'approssimazione di Laplace di terzo ordine per il calcolo della verosimiglianza marginale. Il quinto contributo si focalizza sulla scelta delle statistiche descrittive per ABC e propone un metodo parametrico, basato sulla funzione di score composita, per la scelta di tali statistiche. Infine, l'ultimo contributo si focalizza sulla scelta di una distribuzione di proposta da defalut per algoritmi ABC, dove la procedura di derivazione di tale distributzione è basata sulla nozione della quasi-verosimiglianza.
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Fan, Hang. "Estimation of Species Tree Using Approximate Bayesian Computation." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281732679.

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Prangle, Dennis. "Summary statistics and sequential methods for approximate Bayesian computation." Thesis, Lancaster University, 2011. http://eprints.lancs.ac.uk/62703/.

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Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the simulated data to summary statistics of the observed data. This thesis looks at two related methodological issues for ABC. Firstly a method is proposed to construct appropriate summary statistics for ABC in a semi-automatic manner. The aim is to produce summary statistics which will enable inference about certain parameters of interest to be as accurate as possible. Theoretical results show that, in some sense, optimal summary statistics are the posterior means of the parameters. While these cannot be calculated analytically, an extra stage of simulation is used to estimate how the posterior means vary as a function of the data, and these estimates are then used as summary statistics within ABC. Empirical results show that this is a robust method for choosing summary statistics, that can result in substantially more accurate ABC analyses than previous approaches in the literature. Secondly, ABC inference for multiple independent data sets is considered. If there are many such data sets, it is hard to choose summary statistics which capture the available information and are appropriate for general ABC methods. An alternative sequential ABC approach is proposed in which simulated and observed data are compared for each data set and combined to give overall results. Several algorithms are proposed and their theoretical properties studied, showing that exploiting ideas from the semi-automatic ABC theory produces consistent parameter estimation. Implementation details are discussed, with several simulation examples illustrating these and application to substantive inference problems.
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Campos, Thiago Feitosa. "Aplicações do approximate Bayesian computation a controle de qualidade." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-15102015-174147/.

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Neste trabalho apresentaremos dois problemas do contexto de controle estatístico da qualidade: monitoramento \"on-line\'\' de qualidade e environmental stress screening, analisados pela óptica bayesiana. Apresentaremos os problemas dos modelos bayesianos relativos a sua aplicação e, os reanalisamos com o auxílio do ABC o que nos fornece resultados de uma maneira mais rápida, e assim possibilita análises diferenciadas e a previsão novas observações.<br>In this work we will present two problems in the context of statistical quality control: on line quality monitoring and environmental stress screening, analyzed from the Bayesian perspective. We will present problems of the Bayesian models related to their application, and also we reanalyze the problems with the assistance of ABC methods which provides results in a faster way, and so enabling differentiated analyzes and new observations forecast.
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McCloskey, Rosemary Martha. "Phylogenetic estimation of contact network parameters with approximate Bayesian computation." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58663.

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Models of the spread of disease in a population often make the simplifying assumption that the population is homogeneously mixed, or is divided into homogeneously mixed compartments. However, human populations have complex structures formed by social contacts, which can have a significant influence on the rate and pattern of epidemic spread. Contact networks capture this structure by explicitly representing each contact that could possibly lead to a transmission. Contact network models parameterize the structure of these networks, but estimating their parameters from contact data requires extensive, often prohibitive, epidemiological investigation. We developed a method based on approximate Bayesian computation (ABC) for estimating structural parameters of the contact network underlying an observed viral phylogeny. The method combines adaptive sequential Monte Carlo for ABC, Gillespie simulation for propagating epidemics though networks, and a previously developed kernel-based tree similarity score. Our method offers the potential to quantitatively investigate contact network structure from phylogenies derived from viral sequence data, complementing traditional epidemiological methods. We applied our method to the Barabási-Albert network model. This model incorporates the preferential attachment mechanism observed in real world social and sexual networks, whereby individuals with more connections attract new contacts at an elevated rate (“the rich get richer”). Using simulated data, we found that the strength of preferential attachment and the number of infected nodes could often be accurately estimated. However, the mean degree of the network and the total number of nodes appeared to be weakly- or non-identifiable with ABC. Finally, the Barabási-Albert model was fit to eleven real world HIV datasets, and substantial heterogeneity in the parameter estimates was observed. Posterior means for the preferential attachment power were all sub-linear, consistent with literature results. We found that the strength of preferential attachment was higher in injection drug user populations, potentially indicating that high-degree “superspreader” nodes may play a role in epidemics among this risk group. Our results underscore the importance of considering contact structures when investigating viral outbreaks.<br>Science, Faculty of<br>Graduate
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Simola, Umberto. "Developments in Approximate Bayesian Computation and Statistical Applications in Astrostatistics." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3423284.

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The title of this Thesis embraces two topics that have been investigated. Most of the present work is dedicated to develops and extensions for Approximate Bayesian Computation (ABC). While several algorithms have been proposed to improve the efficiency of the basic ABC algorithm, a number of subjective choices is left to the researcher. Several of these choices have not only an impact on the efficiency of the algorithm but also on its capability to approximate properly the true posterior distribution. We present a first extension of the ABC Population Monte-Carlo (ABC-PMC) algorithm aimed by the goal of minimizing the number of subjective inputs required to the user, improving at the same time the computational efficiency of the algorithm. In the second work we propose extensions of the ABC-PMC algorithm as an alternative framework for inference to work with finite mixture models. The second topic was initiated from a collaboration between the Statistics and Data Science Department and the Astronomy Department at Yale University and the Department of Physics at the University of Geneve, with the goal of detecting and characterizing "Earth-like" extrasolar planets. We propose a novel statistical tool to better disentangle stellar activity from the pure signal coming from an extrasolar planet, aimed by the goal of detecting and characterizing "Earth-like'' planets.<br>Il titolo di questa Tesi vuole abbracciare i due differenti argomenti che sono stati investigati. La maggior parte del presente lavoro é dedicata a sviluppi ed estensioni dell' algoritmo Approximate Bayesian Computation Population Monte-Carlo (ABC-PMC). Mentre parecchi algoritmi sono stati proposti per migliorare l'efficienza della procedura base ABC, alcune scelte soggettive vengono lasciate al ricercatore. Alcune di queste scelte hanno non solo un impatto sull'efficienza dell'algoritmo, ma anche sulla capacità del medesimo di approssimare in maniera consona la vera distribuzione a posteriori. Noi presentiamo una prima estensione dell'algoritmo ABC-PMC che vuole minimizzare il numero di scelte soggettive richieste all'utente, con l'obiettivo di migliorare l'efficienza dell'algoritmo preservando al contempo l'ottenimento di una fedele approssimazione della vera distribuzione a posteriori. Come seconda estensione, proponiamo una procedura basata sull'algoritmo ABC-PMC per lavorare con modelli mistura (caso finito). Il secondo argomento descrive uno dei risultati della collaborazione tra il Dipartimento di Astronomia e il Dipartimento di Statistica e Data Science all' Università di Yale ed il Dipartimento di Fisica all'Università di Ginevra, dove l'obiettivo consiste nello scovare e caratterizzare pianeti extrasolari. Noi proponiamo una nuova tecnica statistica per meglio separare l'attività stellare dal puro segnale proveniente da un pianeta extrasolare, con l'obiettivo di scovare e caratterizzare esopianeti terrestri teoricamente adatti ad ospitare la vita.
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Junior, Ricardo Fernandes Campos. "Divergência populacional e expansão demográfica de Dendrocolaptes platyrostris (Aves: Dendrocolaptidae) no final do Quaternário." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/41/41131/tde-22012013-102652/.

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Dendrocolaptes platyrostris é uma espécie de ave florestal associada às matas de galeria do corredor de vegetação aberta da América do sul (D. p. intermedius) e à Floresta Atlântica (D. p. platyrostris). Em um trabalho anterior, foi observada estrutura genética populacional associada às subespécies, além de dois clados dentro da Floresta Atlântica e evidências de expansão na população do sul, o que é compatível com o modelo Carnaval-Moritz. Utilizando approximate Bayesian computation, o presente trabalho avaliou a diversidade genética de dois marcadores nucleares e um marcador mitocondrial dessa espécie com o objetivo de comparar os resultados obtidos anteriormente com os obtidos utilizando uma estratégia multi-locus e considerando variação coalescente. Os resultados obtidos sugerem uma relação de politomia entre as populações que se separaram durante o último período interglacial, mas expandiram após o último máximo glacial. Este resultado é consistente com o modelo de Carnaval-Moritz, o qual sugere que as populações sofreram alterações demográficas devido às alterações climáticas ocorridas nestes períodos. Trabalhos futuros incluindo outros marcadores e modelos que incluam estabilidade em algumas populações e expansão em outras são necessários para avaliar o presente resultado<br>Dendrocolaptes platyrostris is a forest specialist bird associated to gallery forests of the open vegetation corridor of South America (D. p. intermedius) and to the Atlantic forest (D. p. platyrostris). A previous study showed a population genetic structure associated with the subspecies, two clades within the Atlantic forest, and evidence of population expansion in the south, which is compatible with Carnaval- Moritz\'s model. The present study evaluated the genetic diversity of two nuclear and one mitochondrial markers of this species using approximate Bayesian computation, in order to compare the results previously obtained with those based on a multi-locus strategy and considering the coalescent variation. The results suggest a polytomic relationship among the populations that split during the last interglacial period and expanded after the last glacial maximum. This result is consistent with the model of Carnaval-Moritz, which suggests that populations have undergone demographic changes due to climatic changes that occurred in these periods. Future studies including other markers and models that include stability in some populations and expansion in others are needed to evaluate the present result
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Bergqvist, Oscar. "Calibration of Breast Cancer Natural History Models Using Approximate Bayesian Computation." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273605.

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Natural history models for breast cancer describe the unobservable disease progression. These models can either be fitted using likelihood-based estimation to data on individual tumour characteristics, or calibrated to fit statistics at a population level. Likelihood-based inference using individual level data has the advantage of ensuring model parameter identifiability. However, the likelihood function can be computationally heavy to evaluate or even intractable. In this thesis likelihood-free estimation using Approximate Bayesian Computation (ABC) will be explored. The main objective is to investigate whether ABC can be used to fit models to data collected in the presence of mammography screening. As a background, a literature review of ABC is provided. As a first step an ABC-MCMC algorithm is constructed for two simple models both describing populations in absence of mammography screening, but assuming different functional forms of tumour growth. The algorithm is evaluated for these models in a simulation study using synthetic data, and compared with results obtained using likelihood-based inference. Later, it is investigated whether ABC can be used for the models in presence of screening. The findings of this thesis indicate that ABC is not directly applicable to these models. However, by including a sub-model for tumour onset and assuming that all individuals in the population have the same screening attendance it was possible to develop an ABC-MCMC algorithm that carefully takes individual level data into consideration in the estimation procedure. Finally, the algorithm was tested in a simple simulation study using synthetic data. Future research is still needed to evaluate the statistical properties of the algorithm (using extended simulation) and to test it on observational data where previous estimates are available for reference.<br>Natural history models för bröstcancer är statistiska modeller som beskriver det dolda sjukdomsförloppet. Dessa modeller brukar antingen anpassas till data på individnivå med likelihood-baserade metoder, eller kalibreras mot statistik för hela populationen. Fördelen med att använda data på individnivå är att identifierbarhet hos modellparametrarna kan garanteras. För dessa modeller händer det dock att det är beräkningsintensivt eller rent utav omöjligt att evaluera likelihood-funktionen. Huvudsyftet med denna uppsats är att utforska huruvida metoden Approximate Bayesian Computation (ABC), som används för skattning av statistiska modeller där likelihood-funktionen inte är tillgänglig, kan implementeras för en modell som beskriver bröstcancer hos individer som genomgår mammografiscreening. Som en del av bakgrunden presenteras en sammanfattning av modern ABC-forskning. Metoden består av två delar. I den första delen implementeras en ABC-MCMC algoritm för två enklare modeller. Båda dessa modeller beskriver tumörtillväxten hos individer som ej genomgår mammografiscreening, men modellerna antar olika typer av tumörtillväxt. Algoritmen testades i en simulationsstudie med syntetisk data genom att jämföra resultaten med motsvarande från likelihood-baserade metoder. I den andra delen av metoden undersöks huruvida ABC är kompatibelt med modeller för bröstcancer hos individer som genomgår screening. Genom att lägga till en modell för uppkomst av tumörer och göra det förenklande antagandet att alla individer i populationen genomgår screening vid samma ålder, kunde en ABC-MCMC algoritm utvecklas med hänsyn till data på individnivå. Algoritmen testades sedan i en simulationsstudie nyttjande syntetisk data. Framtida studier behövs för att undersöka algoritmens statistiska egenskaper (genom upprepad simulering av flera dataset) och för att testa den mot observationell data där tidigare parameterskattningar finns tillgängliga.
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Books on the topic "Approximate Bayesian Computation"

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Sisson, S. A., Y. Fan, and M. A. Beaumont, eds. Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195.

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Beaumont, Mark, Scott A. Sisson, and Yanan Fan. Handbook of Approximate Bayesian Computation. Taylor & Francis Group, 2018.

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Beaumont, Mark, Scott A. Sisson, and Yanan Fan. Handbook of Approximate Bayesian Computation. Taylor & Francis Group, 2018.

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Beaumont, Mark, Scott A. Sisson, and Yanan Fan. Handbook of Approximate Bayesian Computation. Taylor & Francis Group, 2018.

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Beaumont, M. A., S. A. Sisson, and Y. Fan. Handbook of Approximate Bayesian Computation. Taylor & Francis Group, 2020.

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Handbook of Approximate Bayesian Computation. Taylor & Francis Group, 2018.

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Beaumont, Mark, Scott A. Sisson, and Yanan Fan. Approximate Bayesian Computation: Likelihood-Free Methods for Complex Models. Taylor & Francis Group, 2018.

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Loos, Carolin. Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation. Springer Spektrum, 2016.

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Loos, Carolin. Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation. Spektrum Akademischer Verlag GmbH, 2016.

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Book chapters on the topic "Approximate Bayesian Computation"

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Martin, Osvaldo A., Ravin Kumar, and Junpeng Lao. "Approximate Bayesian Computation." In Bayesian Modeling and Computation in Python. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003019169-8.

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Plagnol, Vincent, and Simon Tavaré. "Approximate Bayesian Computation and MCMC." In Monte Carlo and Quasi-Monte Carlo Methods 2002. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18743-8_5.

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Chiachío-Ruano, Manuel, Juan Chiachío-Ruano, and María L. Jalón. "Solving Inverse Problems by Approximate Bayesian Computation." In Bayesian Inverse Problems. CRC Press, 2021. http://dx.doi.org/10.1201/b22018-3.

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Sisson, S. A., Y. Fan, and M. A. Beaumont. "Overview of ABC." In Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-1.

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Fearnhead, Paul. "Asymptotics of ABC." In Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-10.

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Ratmann, Oliver, Anton Camacho, Sen Hu, and Caroline Colijn. "Informed Choices: How to Calibrate ABC with Hypothesis Testing." In Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-11.

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Drovandi, Christopher C., Clara Grazian, Kerrie Mengersen, and Christian Robert. "Approximating the Likelihood in ABC." In Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-12.

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Kousathanas, Athanasios, Pablo Duchen, and Daniel Wegmann. "A Guide to General-Purpose ABC Software." In Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-13.

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Barthelmé, Simon, Nicolas Chopin, and Vincent Cottet. "Divide and Conquer in ABC." In Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-14.

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Peters, Gareth W., Efstathios Panayi, and Francois Septier. "Sequential Monte Carlo-ABC Methods for Estimation of Stochastic Simulation Models of the Limit Order Book." In Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-15.

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Conference papers on the topic "Approximate Bayesian Computation"

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Gu, Q., G. Angelis, D. Bailey, et al. "Parametric Maps of Kinetic Heterogeneity and Ki in Dynamic Total Body PET using Approximate Bayesian Computation." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10657628.

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Duewall, Clinton M., and Mahmoud M. El-Halwagi. "Constraint Formulations for Bayesian Optimization of Process Simulations: General Approach and Application to Post-Combustion Carbon Capture." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.170471.

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Abstract:
Some of the most highly trusted and ubiquitous process simulators have solution methods that are incompatible with algorithms designed for equation-oriented optimization. The natively unconstrained Efficient Global Optimization (EGO) algorithm approximates a black-box simulation with kriging surrogate models to convert the simulation results into a reduced-order model more suitable for optimization. This work evaluates several established constraint-handling approaches for EGO to compare their accuracy, computational efficiency, and reliability using an example simulation of an amine post-combustion carbon capture process. While each approach returned a feasible operating point in the number of iterations provided, none of them effectively converged to a solution, exploring the search space without effectively exploiting promising regions. Using the product of expected improvement and probability of feasibility as next point selection criteria resulted in the best solution value and reliability. Constraining probability of feasibility while solving for the next sample point was the least likely to solve, but the solutions found were most likely to be feasible operating points.
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Singh, Prashant, and Andreas Hellander. "HYPERPARAMETER OPTIMIZATION FOR APPROXIMATE BAYESIAN COMPUTATION." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632304.

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Dedecius, Kamil, and Petar M. Djuric. "Diffusion filtration with approximate Bayesian computation." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178563.

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Nadjahi, Kimia, Valentin De Bortoli, Alain Durmus, Roland Badeau, and Umut Simsekli. "Approximate Bayesian Computation with the Sliced-Wasserstein Distance." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054735.

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O'Meara, Brian C., and Barbara Banbury. "APPROXIMATE BAYESIAN COMPUTATION FOR TRAIT EVOLUTION ON PHYLOGENIES." In GSA Annual Meeting in Denver, Colorado, USA - 2016. Geological Society of America, 2016. http://dx.doi.org/10.1130/abs/2016am-287739.

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Batista Godoy, Vitória, Samuel da Silva, and Rafael de Oliveira Teloli. "Data-Driven Bayesian Modeling Using Approximate Bayesian Computation for Heat Exchanger Monitoring." In 20th Brazilian Congress of Thermal Sciences and Engineering. ABCM, 2024. https://doi.org/10.26678/abcm.encit2024.cit24-0725.

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Doronina, Olga, Jason, Christopher, Colin A. Z. Towery, Peter Hamlington, and Werner J. A. Dahm. "Autonomic Closure for Turbulent Flows Using Approximate Bayesian Computation." In 2018 AIAA Aerospace Sciences Meeting. American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-0594.

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Marin, Jean-Michel, Pierre Pudlo, and Mohammed Sedki. "Optimal parallelization of a sequential approximate Bayesian computation algorithm." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465244.

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Bharti, Ayush, and Troels Pedersen. "Calibration of Stochastic Channel Models Using Approximate Bayesian Computation." In 2019 IEEE Globecom Workshops (GC Wkshps). IEEE, 2019. http://dx.doi.org/10.1109/gcwkshps45667.2019.9024563.

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Reports on the topic "Approximate Bayesian Computation"

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Pieterjan, Robbe, Tiernan Casey, Christopher Matthews, et al. Calibration of the diffusivity predictions of Centipede using approximate Bayesian computation and applications in Nyx (engineering scale) and Xolotl-MARMOT (meso-scale) simulations. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1845228.

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