15 papers · ranked by Valyu relevance
Bárbara Andrade Barbosa, Saskia D. van Asten, Ji Won Oh, Arantza Farina-Sarasqueta + 7 more
'Arantza Farina-Sarasqueta' 'Joanne Verheij' 'Frederike Dijk' 'Hanneke W. M. van Laarhoven' 'Bauke Ylstra' 'Juan J. Garcia Vallejo' 'Mark A. van de Wiel' 'Yongsoo Kim'] Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue’s complex cellular make-up, such as the…
Gabriel Torregrosa-Cortés, David Oriola, Vikas Trivedi, Jordi Garcia-Ojalvo
'Jordi Garcia-Ojalvo'] Title: Summary Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions…
Tomohiro Nabika, Kenji Nagata, Masaichiro Mizumaki, Shun Katakami + 1 more
'Masato Okada'] Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments. Therefore, we proposed an active learning with model selection method using multiple parametric…
Bogdan Toader, Jérôme Boulanger, Yury Korolev, Martin O. Lenz + 3 more
'James Manton' 'Carola-Bibiane Schönlieb' 'Leila Mureşan'] We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial variation of the point spread function of a light-sheet microscope is determined…
Ali Mohammad-Djafari, Wolfgang von der Linden, Sascha Ranftl
Classical methods for inverse problems are mainly based on regularization theory, in particular those, that are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and a great number of optimization algorithms have been proposed.…
Oscar Bates, Lluis Guasch, George Strong, Thomas Caradoc Robins + 4 more
'Oscar Calderon-Agudo' 'Carlos Cueto' 'Javier Cudeiro' 'Mengxing Tang'] Bayesian methods are a popular research direction for inverse problems. There are a variety of techniques available to solve Bayes’ equation, each with their own strengths and limitations. Here, we discuss stochastic variational inference (SVI)…
Alain Oliviero-Durmus, Yazid Janati, Eric Moulines, Marcelo Pereyra + 1 more
'Sebastian Reich'] This special issue addresses Bayesian inverse problems using data-driven priors derived from deep generative models (DGMs) and the convergence of generative modelling techniques and Bayesian inference methods. Conventional Bayesian priors often fail to accurately capture the properties and the…
Zhengrong Xing, Peter Carbonetto, Matthew Stephens
Signal denoising-also known as non-parametric regression-is often performed through shrinkage estimation in a transformed (e.g., wavelet) domain; shrinkage in the transformed domain corresponds to smoothing in the original domain. A key question in such applications is how much to shrink, or, equivalently, how much to…
Muyang Zhang, Robert G. Aykroyd, Charalampos Tsoumpas
Medical images are hampered by noise and relatively low resolution, which create a bottleneck in obtaining accurate and precise measurements of living organisms. Noise suppression and resolution enhancement are two examples of inverse problems. The aim of this study is to develop novel and robust estimation approaches…
Jaewon Lee, Tenzing H. Joshi, Mark S. Bandstra, Donald L. Gunter + 3 more
'Brian J. Quiter' 'Reynold J. Cooper' 'Kai Vetter'] We propose a complete framework for Bayesian image reconstruction and uncertainty quantification based on a Gaussian process prior (GPP) to overcome limitations of maximum likelihood expectation maximization (ML-EM) image reconstruction algorithm. The prior…
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Brandon S. Coventry, Edward L. Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Udi Alter, Miranda A. Too, Robert A. Cribbie
Bayesian statistics has gained substantial popularity in the social sciences, particularly in psychology. Despite its growing prominence in the psychological literature, many researchers remain unacquainted with Bayesian methods and their advantages. This tutorial addresses the needs of curious applied psychology…
Matthew Stephens
I discuss the benefits of looking through the ‘Bayesian lens’ (seeking a Bayesian interpretation of ostensibly non-Bayesian methods), and the dangers of wearing ‘Bayesian blinkers’ (eschewing non-Bayesian methods as a matter of philosophical principle). I hope that the ideas may be useful to scientists trying to…
Mark A. Gannon
Students are told in basic probability classes that there are two main “schools” of statistics, the frequentist and the Bayesian, and that those different views of how to approach statistical inference problems arise from two different views of the meaning of probability. Practicing scientists know things are not that…