Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
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…
Kaya Demir, Salih Ergün
This paper presents an analytical study on the use of deterministic chaos as an entropy source for the generation of random numbers. The chaotic signal generated by a phase-locked loop (PLL) device is investigated using numerical simulations. Depending on the system parameters, the chaos originating from the PLL device…
Kailai Li, Florian Pfaff, Uwe D. Hanebeck, Stefano Lenci
In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises-Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed…
Houying Zhu, Josef Dick
We consider an acceptance-rejection sampler based on a deterministic driver sequence. The deterministic sequence is chosen such that the discrepancy between the empirical target distribution and the target distribution is small. We use quasi-Monte Carlo (QMC) point sets for this purpose. The empirical evidence shows…
Alice Milne, Sijia Zhao, Christina Tampakaki, Gabriela Bury + 1 more
The brain is highly sensitive to auditory regularities and exploits the predictable order of sounds in many situations, from parsing complex auditory scenes, to the acquisition of language. To understand the impact of stimulus predictability on perception, it is important to determine how the detection of predictable…
Arya Bangun, Arash Behboodi, Rudolf Mathar
In this paper, the goal is to design deterministic sampling patterns on the sphere and the rotation group and, thereby, construct sensing matrices for sparse recovery of band-limited functions. It is first shown that random sensing matrices, which consists of random samples of Wigner D-functions, satisfy the Restricted…
Stephen DeSalvo
We present a probabilistic divide-and-conquer (PDC) method for exact sampling of conditional distributions of the form L(X | X ∈ E), where X is a random variable on X, a complete, separable metric space, and event E with P(E) ≥ 0 is assumed to have sufficient regularity such that the conditional distribution exists and…
Christoph Hartmann, Andreea Lazar, Jochen Triesch
Trial-to-trial variability and spontaneous activity of cortical recordings have been suggested to reflect intrinsic noise. This view is currently challenged by mounting evidence for structure in these phenomena: Trial-to-trial variability decreases following stimulus onset and can be predicted by previous spontaneous…
François Bachoc
Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter. Consistency and asymptotic normality are proved for the Maximum…
Lorenzo Fattorini, Marzia Marcheselli, Caterina Pisani, Luca Pratelli
'Luca Pratelli'] Title: Abstract Nearest neighbor spatial interpolation for mapping continuous populations and finite populations of areas or units is approached from a design-based perspective, that is, populations are fixed, and uncertainty stems from the sampling scheme adopted to select locations. We derive…
Dominik D. Freydenberger, Markus L. Schmid
Most modern libraries for regular expression matching allow back-references (i. e., repetition operators) that substantially increase expressive power, but also lead to intractability. In order to find a better balance between expressiveness and tractability, we combine these with the notion of determinism for regular…
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…
Marwen Belkaid, Elise Bousseyrol, Romain Durand-de Cuttoli, Malou Dongelmans + 9 more
Can our choices just be driven by chance? To investigate this question, we designed a deterministic setting in which mice reinforce non-repetitive choice sequences, and modeled it using reinforcement learning. Mice progressively increased their choice variability using a memory-free, pseudo-random selection, rather…
Authors not listed
Accurate and efficient calculation of alchemical free energies is a critical challenge in computational chemistry, frequently hindered by the inherent limitations of conventional Thermodynamic Integration (TI) methods. These limitations include poor phasespace overlap between discrete alchemical states, inefficient…
Michał Dereziński, Manfred K. Warmuth, Daniel Hsu
Consider linear regression where the examples are generated by an unknown distribution on R d × R. Without any assumptions on the noise, the linear least squares solution for any i.i.d. sample will typically be biased w.r.t. the least squares optimum over the entire distribution. However, we show that if an i.i.d.…
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…
Yunbum Kook, Matthew S. Zhang
Uniform sampling over a convex body is a fundamental algorithmic problem, yet the convergence in KL or Rényi divergence of most samplers remains poorly understood. In this work, we propose a constrained proximal sampler, a principled and simple algorithm that possesses elegant convergence guarantees. Leveraging the…
Maxime Maheu, Florent Meyniel, Stanislas Dehaene
Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence of a dissociation, in humans, between two systems, one for handling statistical regularities governing the probabilities of individual items and their…
Finlay Clark, Graeme Robb, Daniel Cole, Julien Michel
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a…
Sebastian Schneegans, Robert Taylor, Paul M Bays
Research into human working memory limits has been shaped by the competition between different formal models, with a central point of contention being whether internal representations are continuous or discrete. Here we describe a sampling approach derived from principles of neural coding as a new framework to…
Arthur P. Guillaumin, Adam M. Sykulski, Sofia C. Olhede, Frederik J. Simons
'Frederik J. Simons'] Title: Abstract We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle likelihood, makes important…