25 papers · ranked by Valyu relevance
Francesco Pinotti, Julien Thézé, Xavier Bailly, Guillaume Fournié
Mathematical models play a central role in understanding and forecasting infectious disease dynamics, but parameter inference is often difficult when likelihoods are intractable. Simulation-based inference (SBI) circumvents this limitation by relying on model simulations. Traditional SBI methods, such as Approximate…
Hamid Motallebzadeh, Michael Deistler, Florian M. Schönleitner, Jakob H. Macke + 1 more
Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. Tuning these models is particularly challenging when a large number of parameters are involved. Traditional methods, such as…
Martin Petr, Isabel M. Pötzsch, Fernando Racimo
Simulation-based inference methods such as Approximate Bayesian Computation (ABC) are a popular class of techniques in evolutionary biology and population genetics. These methods are particularly useful for fitting complex models, as they can bypass the need to compute an exact likelihood function, instead relying on…
Shiyi Sun, Geoff K. Nicholls, Jeong Woon Lee
Generalized Bayesian Inference (GBI) tempers a loss with a temperature β > 0 to mitigate overconfidence and improve robustness under model misspecification, but existing GBI methods typically rely on costly MCMC or SDE-based samplers and must be re-run for each new dataset and each β-value. We give the first fully…
Vasilis Gkolemis, Christos Diou, Michael U. Gutmann
Bayesian parameter inference for complex stochastic simulators is challenging due to intractable likelihood functions. Existing simulation-based inference methods often require large number of simulations and become costly to use in high-dimensional parameter spaces or in problems with partially uninformative outputs.…
Xinwei Shen, Diana Cai, Cheng Zhang, David M. Blei
Empirical Bayes (EB) performs simultaneous inference across many related latent variables. Classical EB assumes that the likelihood p(x | z) is tractable. In many scientific applications, however, the likelihood is available only through a simulator. This paper develops EB for such implicit likelihoods. We introduce…
Nadav Ben Nun, Saharon Rosset, David Gresham, Yoav Ram
High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime…
Geunsoo Jang, K. Selçuk Candan, Gerardo Chowell, Christopher E Miles
Epidemic models play a critical role in understanding transmission dynamics, generating forecasts, and informing public health interventions when they are properly calibrated to epidemiological data. Traditional Bayesian inference methods rely on the likelihood function to update prior knowledge using observed data.…
Tom Kimpson, Jennifer Flegg, Matthew J. Simpson
Cell migration is a key biological process underlying wound healing, tissue development, and cancer metastasis, yet calibrating mathematical models of migration to experimental data remains a major challenge. Scratch and barrier assays are widely used to study collective cell spreading, and agent-based random walk…
Aurelio Amerio
Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation-based inference (SBI), extending naturally from neural posterior estimation to likelihood and joint density estimation. Their principled optimization objectives and freedom from architectural constraints…
Aurélien Tauzin, Antoine Frenoy
Bacterial mutation rates are traditionally inferred from phenotypic data using fluctuation assays. Mutation rate computation from these assays relies on a mathematical model describing the emergence of mutants during population growth. However, this standard model relies on restrictive assumptions that are often…
Alex Ocampo, Enrico Giudice, Zachary R. McCaw, Tim P. Morris
Simulation studies are used to understand the properties of statistical methods. A key luxury in many simulation studies is knowledge of the true value (i.e. the estimand) being targeted. With this oracle knowledge in-hand, the researcher conducting the simulation study can assess across repeated realizations of the…
Maria Wirth, Andreas Voss, Stefan T. Radev, Klaus Rothermund
No matter how angry, sad, or happy we are, eventually, we will feel different. Studying this ebb and flow of affective experience in daily life provides important insights into psychological functioning and well-being. We have developed a parsimonious formalized model of intraindividual variability in affect (MIVA)…
Hui Yuan, Ligang Wang, Wenbin Gao, Ting Tao + 2 more
This review systematically explores the potential of the active inference framework in illuminating the cognitive mechanisms of decision-making in repeated games. Repeated games, characterized by multi-round interactions and social uncertainty, closely resemble real-world social scenarios in which the decision-making…
L. M. André, J. L. Wadsworth, R. Huser
Likelihood-free approaches are appealing for performing inference on complex dependence models, either because it is not possible to formulate a likelihood function, or its evaluation is very computationally costly. This is the case for several models available in the multivariate extremes literature, particularly for…
Nadav Ben Nun, Saharon Rosset, David Gresham, Yoav Ram + 1 more
High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime…
Trevelyan J. McKinley, Daniel B. Williamson, Xiaoyu Xiong, James M. Salter + 5 more
Calibration of complex stochastic infectious disease models is challenging. These often have high-dimensional input and output spaces, with the models exhibiting complex, non-linear dynamics. Coupled with a paucity of necessary data, this results in a large number of non-ignorable hidden states that must be handled by…
Eric T. Lofgren, Kellen Myers, Nina H. Fefferman
Computational simulation provides a powerful toolkit for in silico experimentation. However, while the field has developed best practices for the design and implementation of such models, there remains ambiguity in discussions about how to understand and/or interpret their results due to their inherent ability to…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
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The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
David Gómez-Guillén, Mireia Diaz, Josep Lluis Arcos, Jesús Cerquides
Calibration of grey-box simulation models is a constrained optimization problem in which model evaluations are expensive, the parameter space can be high-dimensional, and the search must respect plausibility constraints. Although the simulation code is fully available to the analyst, the joint effect of multiple…
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Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…
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Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
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High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
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Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…