23 papers · ranked by Valyu relevance
Milena Rmus, Ti-Fen Pan, Liyu Xia, Anne G. E. Collins
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best…
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…
Fuad S. Alduais, Neveen Sayed-Ahmed
The most essential process in statistical image and signal processing is the parameter estimation of probability density functions (PDFs). The estimation of the probability density functions is a contentious issue in the domains of artificial intelligence and machine learning. The study examines challenges related to…
William Green
A longstanding project of the chemical kinetics community is to predict reaction rates and the behavior of reacting systems, even for systems where there are no experimental data. Many important reacting systems (atmosphere, combustion, pyrolysis, partial oxidations) involve a large number of reactions occurring…
Jan Boelts, Jan-Matthis Lueckmann, Richard Gao, Jakob H. Macke
Identifying parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference aims to not only find a single configuration of best-fitting parameters, but to recover all model parameters that are consistent with the data and prior knowledge.…
Jing-Jing Li, Chengchun Shi, Lexin Li, Anne G. E. Collins
Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise…
Dana Naderi, Christian P Robert, Kaniav Kamary, Darren Wraith
Efficient Bayesian model selection relies on the model evidence or marginal likelihood, whose computation often requires evaluating an intractable integral. The harmonic mean estimator (HME) has long been a standard method of approximating the evidence. While computationally simple, the version introduced by Newton and…
Christina Schenk, Ignacio Romero
In this work, we review the theory involved in the Bayesian calibration of complex computer models, with particular emphasis on their use for applications involving computationally expensive simulations and scarce experimental data. In the article, we present a unified framework that incorporates various Bayesian…
Linke Li, Hawre Jalal, Anna Heath
In this section, we review current $n_{0}$ estimation methods and note their drawbacks.2 We then introduce a novel approach, which uses nonparametric regression models, to efficiently and accurately estimate $n_{0}$. In the original GA article, Jalal and Alarid-Escudero2 introduced 2 methods that can be generalized to…
Nina Baldy, Martin Breyton, Marmaduke M. Woodman, Viktor K. Jirsa + 1 more
The process of making inference on networks of spiking neurons is crucial to decipher the underlying mechanisms of neural computation. Mean-field theory simplifies the interactions between neurons to produce macroscopic network behavior, facilitating the study of information processing and computation within the brain.…
Amaryllis Mavragani, Jessica Hinman, Alessandro Perrella, Mansour Fahimi + 8 more
Background Increasingly, survey researchers rely on hybrid samples to improve coverage and increase the number of respondents by combining independent samples. For instance, it is possible to combine 2 probability samples with one relying on telephone and another on mail. More commonly, however, researchers are now…
Simon Martina-Perez, Heba Sailem, Ruth E. Baker
Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to…
David Refaeli, Mira Marcus-Kalish, David M. Steinberg
Simulation-Based Inference (SBI) deals with statistical inference in problems where the data are generated from a system that is described by a complex stochastic simulator. The challenge for inference in these problems is that the likelihood is intractable; SBI proceeds by using the simulator to sample from the…
Margret Westerkamp, Jakob Roth, Philipp Frank, Will Handley + 2 more
'Torsten Enßlin' 'Boris Ryabko'] Nested sampling (NS) is a stochastic method for computing the log-evidence of a Bayesian problem. It relies on stochastic estimates of prior volumes enclosed by likelihood contours, which limits the accuracy of the log-evidence calculation. We propose to transform the prior volume…
Xinqiang Ding, John Drohan
A common approach for computing free energy differences among multiple states is to build a perturbation graph connecting the states and compute free energy differences on all edges of the graph. Such perturbation graphs are often designed to have cycles. Because free energy is a function of states, the free energy…
Authors not listed
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…
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Finite-temperature lattice free energy differences between polymorphs of molecular crystals are fundamental to understanding and predicting the relative stability relationships underpinning polymorphism, yet are computationally expensive to obtain. Here, we implement and critically assess machine-learning-enabled…
John Y Choe, Yen-Chi Chen, Nick Terry, Alberto Maria Metelli
This paper considers the problem of approximating an unknown density when it can be evaluated up to a normalizing constant at a finite number of points. This density approximation problem is ubiquitous in statistics, such as approximating a posterior density for Bayesian inference and estimating an optimal density for…
Anirban Bhattacharya, Antonio R. Linero, Chris J. Oates
Computation is arguably one of the fastest evolving subfields of Bayesian statistics at the moment, driven by a combination of democratised access to computing technologies (such as automatic differentiation) and recent algorithmic advancement. While the many successes of Bayesian computation are well-publicised at…
Piotr Gwiazda, Alexey Kazarnikov, Anna Marciniak-Czochra, Zuzanna Szymańska
Calibrating mathematical models of biological processes is essential for achieving predictive accuracy and gaining mechanistic insight. However, this task remains challenging due to limited and noisy data, significant biological variability, and the computational complexity of the models themselves. In this method’s…
Li Yezhuo, Zhang Qiong, Limaye, Madhura + 2 more
Decision-making in manufacturing often involves optimizing key process parameters using data collected from simulation experiments. Gaussian processes are widely used to surrogate the underlying system and guide optimization. Uncertainty often inherent in the decisions given by the surrogate model due to limited data…
Antik Chakraborty, Jonelle Walsh, Louis E. Strigari, Bani K. Mallick + 1 more
the Schwarzschild model Authors: ['Antik Chakraborty' 'Jonelle Walsh' 'Louis E. Strigari' 'Bani K. Mallick' 'Anirban Bhattacharya'] The orbital superposition method originally developed by Schwarzschild (1979) is used to study the dynamics of growth of a black hole and its host galaxy, and has uncovered new…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…