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We provide another look at the statistical calibration problem in computer models. This viewpoint is inspired by two overarching practical considerations of computer models: (i) many computer models are inadequate for perfectly modeling…

Methodology · Statistics 2018-09-26 Xiaowu Dai , Peter Chien

A framework is presented for fitting inverse problem models via variational Bayes approximations. This methodology guarantees flexibility to statistical model specification for a broad range of applications, good accuracy and reduced model…

Methodology · Statistics 2024-09-05 Luca Maestrini , Robert G. Aykroyd , Matt P. Wand

To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is…

Computation · Statistics 2018-08-03 Jonathan U Harrison , Ruth E Baker

We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. More specifically, we consider a Bayesian analysis for the…

Computation · Statistics 2021-07-27 L. Martino , F. Llorente , E. Curbelo , J. Lopez-Santiago , J. Miguez

Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to…

Machine Learning · Statistics 2019-09-12 Tomasz Kuśmierczyk , Joseph Sakaya , Arto Klami

Comparing competing mathematical models of complex natural processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for…

Experiments on decision making under uncertainty are known to display a classical pattern of risk aversion and risk seeking referred to as "fourfold pattern" (or "reflection effect") , but recent experiments varying the speed and order of…

Neurons and Cognition · Quantitative Biology 2024-01-17 Francesco Fumarola , Lukasz Kusmierz , Ronald B. Dekker

Standard causal inference characterizes treatment effect through averages, but the counterfactual distributions could be different in not only the central tendency but also spread and shape. To provide a comprehensive evaluation of…

Methodology · Statistics 2022-11-04 Steven G. Xu , Shu Yang , Brian J. Reich

Developing reliable interatomic potential models with quantified predictive accuracy is crucial for atomistic simulations. Commonly used potentials, such as those constructed through the embedded atom method (EAM), are derived from…

Materials Science · Physics 2022-08-05 Arun Hegde , Elan Weiss , Wolfgang Windl , Habib N. Najm , Cosmin Safta

The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. However, these tissue properties are spatially varying across the underlying anatomical model,…

Machine Learning · Statistics 2020-05-19 Jwala Dhamala , Sandesh Ghimire , John L. Sapp , B. Milan Horácek , Linwei Wang

This study combines a one-dimensional (1D) model with micro-CT imaging and hemodynamic data to quantify uncertainty of flow and pressure predictions in the pulmonary arteries in a control and hypoxia induced hypertensive mouse. We use local…

Fluid Dynamics · Physics 2019-10-02 Mitchel J. Colebank , M. Umar Qureshi , Mette S. Olufsen

Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac…

Computer Vision and Pattern Recognition · Computer Science 2022-08-09 Lei Li , Julia Camps , Abhirup Banerjee , Marcel Beetz , Blanca Rodriguez , Vicente Grau

We focus on improving the accuracy of an approximate model of a multiscale dynamical system that uses a set of parameter-dependent terms to account for the effects of unresolved or neglected dynamics on resolved scales. We start by…

Computational Physics · Physics 2019-06-26 Balasubramanya T. Nadiga , Chiyu Jiang , Daniel Livescu

Predictive dynamical models for marine ecosystems are used for a variety of needs. Due to sparse measurements and limited understanding of the myriad of ocean processes, there is however significant uncertainty. There is model uncertainty…

Computational Engineering, Finance, and Science · Computer Science 2023-06-06 Abhinav Gupta , Pierre F. J. Lermusiaux

Bayesian inference is a widely used technique for real-time characterization of quantum systems. It excels in experimental characterization in the low data regime, and when the measurements have degrees of freedom. A decisive factor for its…

Quantum Physics · Physics 2025-07-10 Alexandra Ramôa , Raffaele Santagati , Nathan Wiebe

The circulatory system, comprising the heart and blood vessels, is vital for nutrient transport, waste removal, and homeostasis. Traditional computational models often treat cardiac electromechanics and blood flow dynamics separately,…

Likelihood-based inference in stochastic non-linear dynamical systems, such as those found in chemical reaction networks and biological clock systems, is inherently complex and has largely been limited to small and unrealistically simple…

Computation · Statistics 2024-07-08 Ben Swallow , David A. Rand , Giorgos Minas

We consider a novel paradigm for Bayesian testing of hypotheses and Bayesian model comparison. Our alternative to the traditional construction of posterior probabilities that a given hypothesis is true or that the data originates from a…

Methodology · Statistics 2019-01-01 Kaniav Kamary , Kerrie Mengersen , Christian P. Robert , Judith Rousseau

Probabilistic estimation of cardiac electrophysiological model parameters serves an important step towards model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes…

In operational weather models, the effects of turbulence in the atmospheric boundary layer (ABL) on the resolved flow are modeled using turbulence parameterizations. These parameterizations typically use a predetermined set of model…

Fluid Dynamics · Physics 2025-05-27 E. Y. Shin , M. F. Howland