24 papers · ranked by Valyu relevance
Lorenzo Tomaselli, Valérie Ventura, Larry Wasserman
Simulation-Based Inference (SBI) is an approach to statistical inference where simulations from an assumed model are used to construct estimators and confidence sets. SBI is often used when the likelihood is intractable and to construct confidence sets that do not rely on asymptotic methods or regularity conditions.…
K. Cranmer, Johann Brehmer, Gilles Louppe
Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces…
Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro + 3 more
'Ozan Şener' 'Marco Cuturi' 'Jörn-Henrik Jacobsen'] Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems insilico. While such tools are routinely used to simulate whole-body hemodynamics from physiological parameters, solving the…
Paul-Christian Bürkner, Marvin Schmitt, Stefan T. Radev
Simulations play important and diverse roles in statistical workflows, for example, in model specification, checking, validation, and even directly in model inference. Over the past decades, the application areas and overall potential of simulations in statistical workflows have expanded significantly, driven by the…
Joonha Park
In many applications, a stochastic system is studied using a model implicitly defined via a simulator. We develop a simulation-based parameter inference method for implicitly defined models. Our method differs from traditional likelihood-based inference in that it uses a metamodel for the distribution of a…
François Rousset, Raphaël Leblois, Arnaud Estoup, Jean-Michel Marin
Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface…
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…
Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski + 1 more
'Jukka Corander'] Title: Abstract Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference…
François Rousset, Raphäel Leblois, Arnaud Estoup, Jean-Michel Marin
Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface…
Kris Sankaran, Susan Holmes
By linking conceptual theories with observed data, generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular biology, theory building in particle physics, and resource allocation in…
Niklas Smedemark-Margulies, Robin Walters, Heiko Zimmermann, Lucas Laird + 5 more
'Lucas Laird' 'Christian van der Loo' 'Neela Kaushik' 'Rajmonda Caceres' 'Jan-Willem van de Meent' 'Tom Britton'] Accurate epidemiological models require parameter estimates that account for mobility patterns and social network structure. We demonstrate the effectiveness of probabilistic programming for parameter…
Maya Horii, Aidan Gould, Zachary Yun, Jaideep Ray + 3 more
'Tarek Zohdi' 'Junhuan Zhang'] Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a…
Andreas Christ Sølvsten Jørgensen, Atiyo Ghosh, Marc Sturrock, Vahid Shahrezaei
The modelling of many real-world problems relies on computationally heavy simulations. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue…
Richard J. Boys, Holly F. Ainsworth, Colin S. Gillespie
Stochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be estimated from observed data. Ideally we would have measurements on all interacting chemical species in the process, observed continuously…
Andreas Christ Sølvsten Jørgensen, Atiyo Ghosh, Marc Sturrock, Vahid Shahrezaei + 1 more
'Vahid Shahrezaei' 'Mark Alber'] The modelling of many real-world problems relies on computationally heavy simulations of randomly interacting individuals or agents. However, the values of the parameters that underlie the interactions between agents are typically poorly known, and hence they need to be inferred from…
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…
Timo M Deist, Andrew Patti, Zhaoqi Wang, David Krane + 3 more
We introduce simulation as a pre-processing step in a machine learning pipeline, in particular, as a way to include expert prior knowledge. One can consider simulation as a technique which regularizes data or as a specialized feature extraction method. In either view, the SimKern methodology offers a decomposition of…
Alex N Popinga, Jack Forman, Dmitri Svetlov, Huy Vo + 1 more
Biological data is prone to both intrinsic and extrinsic noise and variability between experimental replicas. That same stochasticity and heterogeneity can carry information about underlying biochemical mechanisms but, if not incorporated in modeling and probabilistic inference, can also bias parameter estimates and…
Ingo Rohlfing
QCA has recently been subject to massive criticism and although the substance of that criticism is not completely new, it differs from earlier critiques by invoking simulations for the evaluation of QCA. In addition to debates about the meaning of the simulation results, there is a more fundamental discussion about…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Authors not listed
Electrochemiluminescence (ECL) is a vital analytical technique widely used in immunosensing and emerging applica-tions in biological imaging. Traditional ECL simulations rely on finite element methods, which provide valuable insights into reaction dynamics and spatial distribution of species. However, such methods are…
Zhimian Hao, Chonghuan Zhang, Alexei Lapkin
We propose a workflow for reduction in the time required for data generation during generation of statistical digital twins. This methodology is particularly relevant for real-world engineering problems when data generation is expensive. A prerequisite for building surrogates is sufficient input/output data, whereas…
Jonas Verhellen
In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 12, 3567-3572] have, however, been shown to rival deep learning…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…