Approximate Methods for Bayesian Computation

Craiu, Radu V.; Levi, Evgeny

doi:10.1146/annurev-statistics-033121-110254

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…Simulation-based inference will be even more important if the likelihood cannot be specified, for example if our models try to accommodate complex sampling schemes or population effects (e.g., as could arise from frequency-dependent fitness). In this case, Approximate Bayesian Computation and other simulation-based likelihood-free procedures might be necessary (e.g., Mo and Siepel, 2023;Craiu and Levi, 2023;Sainsbury-Dale et al, 2023;Cranmer et al, 2020, and references therein). To begin exploring these approaches, models with stochastic dependencies are probably much easier to play around with.…”

Section: Model Fitting: Probabilistic Programming and Simulation-base...mentioning

confidence: 99%

EvAM-Tools: tools for evolutionary accumulation and cancer progression models

Dı́az-Uriarte

Herrera-Nieto

2022

Preprint

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EvAM-Tools is an R package and web application that provides a unified interface to state-of-the-art cancer progression models (CPMs) and, more generally, evolutionary models of event accumulation. The output includes, in addition to the fitted models, the transition (and transition rate) matrices between genotypes and the probabilities of evolutionary paths. Generation of random cancer progression models is also available. Using the GUI in the web application users can easily construct models (modifying Directed Acyclic Graphs —DAGs— of restrictions, matrices of mutual hazards, or specifying genotype composition), generate data from them (with user-specified observational/genotyping error), and analyze the data.Availability and ImplementationImplemented in R and C; open source code available under the GNU Affero General Public License v3.0 at https://github.com/rdiaz02/EvAM-tools. Docker images freely available from https://hub.docker.com/u/rdiaz02. Web app freely accessible at https://iib.uam.es/evamtools.Contactr.diaz@uam.es

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Section: Model Fitting: Probabilistic Programming and Simulation-base...mentioning

confidence: 99%

EvAM-Tools: tools for evolutionary accumulation and cancer progression models

Dı́az-Uriarte

Herrera-Nieto

2022

Preprint

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“…Indeed, the selection of summary statistics is an important issue when they are not sufficient for the given model. The ABC field has received many reviews due to its wide theoretical scope and applicability (e.g., Sunnåker et al 2013;Karabatsos & Leisen 2018;Sisson et al 2018;Grazian & Fan, 2020;Cranmer et al 2020;Craiu & Levi, 2022;Karabatsos 2023;Pesonen et al 2023;Martin et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Approximate Bayesian computation using asymptotically normal point estimates

Karabatsos¹

2022

Comput Stat

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This paper introduces a novel Approximate Bayesian Computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihood functions. This framework can describe the possibly skewed and high dimensional posterior distribution by a novel multivariate copula-based distribution, based on univariate marginal posterior distributions which can account for skewness and be accurately estimated by Distribution Random Forests (DRF) while performing automatic summary statistics (covariates) selection, and on robustly estimated copula dependence parameters. The framework employs a novel multivariate mode estimator to perform for MLE and posterior mode estimation, and provides an optional step to perform model selection from a given set of models with posterior probabilities estimated by DRF. The posterior distribution estimation accuracy of the ABC framework is illustrated through simulation studies involving models with analytically computable posterior distributions, and involving exponential random graph and mechanistic network models which are each defined by an intractable likelihood from which it is costly to simulate large network datasets. Also, the framework is illustrated through analyses of large reallife networks of sizes ranging between 28,000 to 65.6 million nodes (between 3 million to 1.8 billion edges), including a large multilayer network with weighted directed edges.

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EVCA Classifier: A MCMC-Based Classifier for Analyzing High-Dimensional Big Data

Vlachou

Karras

et al. 2023

Information

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In this work, we introduce an innovative Markov Chain Monte Carlo (MCMC) classifier, a synergistic combination of Bayesian machine learning and Apache Spark, highlighting the novel use of this methodology in the spectrum of big data management and environmental analysis. By employing a large dataset of air pollutant concentrations in Madrid from 2001 to 2018, we developed a Bayesian Logistic Regression model, capable of accurately classifying the Air Quality Index (AQI) as safe or hazardous. This mathematical formulation adeptly synthesizes prior beliefs and observed data into robust posterior distributions, enabling superior management of overfitting, enhancing the predictive accuracy, and demonstrating a scalable approach for large-scale data processing. Notably, the proposed model achieved a maximum accuracy of 87.91% and an exceptional recall value of 99.58% at a decision threshold of 0.505, reflecting its proficiency in accurately identifying true negatives and mitigating misclassification, even though it slightly underperformed in comparison to the traditional Frequentist Logistic Regression in terms of accuracy and the AUC score. Ultimately, this research underscores the efficacy of Bayesian machine learning for big data management and environmental analysis, while signifying the pivotal role of the first-ever MCMC Classifier and Apache Spark in dealing with the challenges posed by large datasets and high-dimensional data with broader implications not only in sectors such as statistics, mathematics, physics but also in practical, real-world applications.

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Approximate Methods for Bayesian Computation

Cited by 4 publications

References 57 publications

EvAM-Tools: tools for evolutionary accumulation and cancer progression models

EvAM-Tools: tools for evolutionary accumulation and cancer progression models

Approximate Bayesian computation using asymptotically normal point estimates

EVCA Classifier: A MCMC-Based Classifier for Analyzing High-Dimensional Big Data

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