ABC of the future

Pesonen, Henri; Simola, Umberto; Köhn‐Luque, Alvaro; Vuollekoski, Henri; Lai, Xiaoran; Frigessi, Arnoldo; Kaski, Samuel; Frazier, David T.; Maneesoonthorn, Worapree; Martin, Gael M.; Corander, Jukka

doi:10.1111/insr.12522

Cited by 6 publications

(8 citation statements)

References 101 publications

(158 reference statements)

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“…This intuitive method has been used for applied work by McKinley et al (2009), Canale and Ruggiero (2016), Pesonen et al (2022) but without detailed analysis. Frazier et al (2019) analyzed this approach theoretically and showed that under certain technical conditions the approximate posterior predictive in a sense coincidences with the exact one as the data size becomes arbitrarily large and as h → 0.…”

Section: Tractable Simulation From Conditional Predictive Densitymentioning

confidence: 99%

“…Despite substantive amount of research on ABC methodology and applications (see e.g. Marin et al 2012;Sisson et al 2019;Pesonen et al 2022) and the fundamental importance of prediction in statistical inference, the use of ABC in the context of predictive inference has not been widely studied. In this paper we investigate ABC as a generic technique to approximate the posterior predictive distribution of some future observations or missing data.…”

Section: Introductionmentioning

confidence: 99%

“…An earlier approach for ABC prediction, used in applied work e.g. by McKinley et al (2009), Canale and Ruggiero (2016), Pesonen et al (2022) and studied theoretically in the context of financial time-series models that facilitate partial analytical treatment by Frazier et al (2019), consists of first using ABC to approximate π(θ | y) and then simulating directly from π( ỹ | y, θ). However, the latter step can be infeasible or its implementation may require laborious model-specific derivations.…”

Section: Introductionmentioning

confidence: 99%

“…Picchini 2014;Kypraios et al 2017;Tancredi 2019;Wilkinson 2019;Buckwar et al 2020) and where incomplete measurements are often only available. See also Pesonen et al (2022) where realistic intractable dynamic models are used for prediction, though in a simpler setting as considered here. The focus of this paper is, however, on understanding the predictive ABC approximations and not on particular applications.…”

Section: Introductionmentioning

confidence: 99%

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On predictive inference for intractable models via approximate Bayesian computation

Järvenpää

Corander

2023

Stat Comput

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Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based statistical models whose likelihood function cannot be evaluated. In this paper we instead investigate the feasibility of ABC as a generic approximate method for predictive inference, in particular, for computing the posterior predictive distribution of future observations or missing data of interest. We consider three complementary ABC approaches for this goal, each based on different assumptions regarding which predictive density of the intractable model can be sampled from. The case where only simulation from the joint density of the observed and future data given the model parameters can be used for inference is given particular attention and it is shown that the ideal summary statistic in this setting is minimal predictive sufficient instead of merely minimal sufficient (in the ordinary sense). An ABC prediction approach that takes advantage of a certain latent variable representation is also investigated. We additionally show how common ABC sampling algorithms can be used in the predictive settings considered. Our main results are first illustrated by using simple time-series models that facilitate analytical treatment, and later by using two common intractable dynamic models.

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Section: Tractable Simulation From Conditional Predictive Densitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On predictive inference for intractable models via approximate Bayesian computation

Järvenpää

Corander

2023

Stat Comput

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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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Amortised Design Optimization for Item Response Theory

Keurulainen

Westerlund²,

Keurulainen³

et al. 2023

Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Track

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I was fortunate to engage in numerous meetings, brainstorming sessions, and discussions with a variety of colleagues at Aalto University. I wish to express my gratitude to Jyri Kivinen, Vikas Verma, Mert Celikok, Tomi Peltola, and all the members of the Probabilistic Machine Learning (PML) group for their insightful and valuable contributions to these discussions. Furthermore, I had the pleasure of participating in many enriching conversations with Professor Antti Oulasvirta and the members of the Computational Behavior Lab at Aalto, led by Professor Oulasvirta.I am also grateful to Isak Westerlund, who has worked alongside me during my PhD project, taking on a significant amount of responsibility in coding the applications and training the various AI models. My thanks also go to Ariel Kwiatkowski, who contributed to our second paper.I am deeply grateful to the pre-examiners, Professor Anna Rafferty and Dr. Xiaoyang Wang, for their essential insights and constructive feedback on my thesis. In addition, I am thankful to Dr. John Williamson for willingly taking on the role of the opponent I want to thank Bitville Oy CEO Jouko Ranta, as well as all my colleagues at Bitville Oy, for making it possible for me to concentrate practically fulltime on the research projects during these years.I had the unique opportunity to share my research journey with my son, Oskar, who has now embarked on his own PhD journey at Aalto University as I write this preface. Thank you for the hundreds of hours of brainstorming, discussion, and reflection over nearly the last 10 years, and for your invaluable contribution as a member of the research team. Our discussions have been of the utmost importance to me, whether they were about mathematical details or the extensive influence of AI on society.Last but not least, I want to extend my heartfelt thanks to my daughter, Erika, and my wife, Sofia, for their encouragement and patience. I am aware that discussions about AI have prominently featured in our family conversations over the last few years. It is evident that this project could not have reached its conclusion without your unwavering support.

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ABC of the future

Cited by 6 publications

References 101 publications

On predictive inference for intractable models via approximate Bayesian computation

On predictive inference for intractable models via approximate Bayesian computation

Approximate Bayesian computation using asymptotically normal point estimates

Amortised Design Optimization for Item Response Theory

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