23 papers · ranked by Valyu relevance
Maxime Vono, Nicolas Dobigeon, Pierre Chainais
Efficient sampling from a high-dimensional Gaussian distribution is an old but highstake issue. Vanilla Cholesky samplers imply a computational cost and memory requirements which can rapidly become prohibitive in high dimension. To tackle these issues, multiple methods have been proposed from different communities…
Bach Do, Nafeezat A. Ajenifuja, Taiwo A. Adebiyi, Ruda Zhang
High-fidelity simulations and physical experiments are essential for engineering analysis and design. However, their high cost often limits their applications in two critical tasks: global sensitivity analysis (GSA) and optimization. This limitation motivates the common use of Gaussian processes (GPs) as proxy…
Daniela Calvetti, Erkki Somersalo
of Linear Inverse Problems Authors: ['Daniela Calvetti' 'Erkki Somersalo'] It is well-known that the posterior density of linear inverse problems with Gaussian prior and Gaussian likelihood is also Gaussian, hence completely described by its covariance and expectation. Sampling from a Gaussian posterior may be…
Paul Masset, Jacob A. Zavatone-Veth, J. Patrick Connor, Venkatesh N. Murthy + 1 more
For animals to navigate an uncertain world, their brains need to estimate uncertainty at the timescales of sensations and actions. Sampling-based algorithms afford a theoretically-grounded framework for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in…
Singh, Gurprit, Jakob, Wenzel
Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. These advances are largely due to diffusion-based generative models, which are very stable and simple to train. These diffusion models are tasked to learn the underlying unknown distribution of the…
Shiliang Sun, Jing Zhao, Minghao Gu, Shanhu Wang + 1 more
The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics. Since HMC uses the gradient information of the target distribution, it can explore the state space much…
M. Rule, P. Chaudhuri-Vayalambrone, M. Krstulovic, M. Bauza + 2 more
We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid…
Yuanzheng Zhu, Antonio M. Scarfone
Sampling from constrained distributions has posed significant challenges in terms of algorithmic design and non-asymptotic analysis, which are frequently encountered in statistical and machine-learning models. In this study, we propose three sampling algorithms based on Langevin Monte Carlo with the Metropolis-Hastings…
Vojko Pjanovic, Jacob Zavatone-Veth, Paul Masset, Sander Keemink + 1 more
Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior effectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to…
Félix Mercier, Nizar Bouhlel, Angelina El Ghaziri, Joseph Ly Vu + 2 more
Digital phenotyping is rapidly advancing, generating increasing amounts of data, particularly in the case of temporal monitoring. We propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage. The proposed method is based on Bayesian…
Adam N. Sanborn, Jian-Qiao Zhu, Jake Spicer, Pablo León-Villagrá + 5 more
'Lucas Castillo' 'Johanna K. Falbén' 'Yun-Xiao Li' 'Aidan Tee' 'Nick Chater'] Noise in behavior is often considered a nuisance: Although the mind aims for the best possible action, it is let down by unreliability in the sensory and response systems. Researchers often represent noise as additive, Gaussian, and…
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…
Allen Ross, Jason Lloyd-Price, Ali Rahnavard
Identifying meaningful associations from small-sample longitudinal data is challenging, especially in low signal-to-noise environments where the Gaussian likelihood assumption does not hold. We introduce two methods to algorithmically perform variable selection with sparse, irregularly sampled, longitudinal count data…
Chandrika Kamath
Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from…
Anthony Trezza, Donald J. Bucci, Pramod K. Varshney
—Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently…
Fabian Böhm, Diego Alonso-Urquijo, Guy Verschaffelt, Guy Van der Sande
'Guy Van der Sande'] Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization. However, while various neural networks can be implemented with Ising machines, their inability to perform fast statistical sampling makes them inefficient for training…
Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist, Simon Olsson
The accurate prediction of thermodynamic properties is crucial in various fields such as drug discovery and materials design. This task relies on sampling from the underlying Boltzmann distribution, which is challenging using conventional approaches such as simulations. In this work, we introduce Surrogate…
Elba Raimúndez, Michael Fedders, Jan Hasenauer
Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter uncertainties, and thereby the reliability of models and their predictions. Yet, generating representative samples from the Bayesian posterior distribution is often computationally…
Emmanuel Ren, François-Xavier Coudert
Molecular adsorption in nanoporous materials has many large-scale industrial applications ranging from separation to storage. To design the best materials, computational simulations are key in guiding the experimentation and engineering processes. Because nanoporous materials exist in a plethora of forms, we need to…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Luke Allan, Tim Zuehlsdorff
The second order cumulant method offers a promising pathway to predicting optical properties in condensed phase systems. It allows for the computation of linear absorption spectra from excitation energy fluctuations sampled along molecular dynamics (MD) trajectories, fully accounting for vibronic effects, direct…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Muhammad Azeem, Sheetal Kalyani
Systematic sampling is one of the simplest and popular methods for selecting a random sample from a finite population. The diagonal systematic sampling scheme is a type of systematic sampling design which has gained the attention of researchers during the last two decades. In this paper, a modification to the…