14 papers · ranked by Valyu relevance
Yuebo Zha, Yulin Huang, Zhichao Sun, Yue Wang + 2 more
'James Churnside'] Scanning radar is of notable importance for ground surveillance, terrain mapping and disaster rescue. However, the angular resolution of a scanning radar image is poor compared to the achievable range resolution. This paper presents a deconvolution algorithm for angular super-resolution in scanning…
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…
Zhe Chen
Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing…
Alexander Wong, Xiao Yu Wang, Maud Gorbet
Fluorescence microscopy is widely used for the study of biological specimens. Deconvolution can significantly improve the resolution and contrast of images produced using fluorescence microscopy; in particular, Bayesian-based methods have become very popular in deconvolution fluorescence microscopy. An ongoing…
Piergiorgio Caramazza, Kali Wilson, Genevieve Gariepy, Jonathan Leach + 3 more
'Jonathan Leach' 'Stephen McLaughlin' 'Daniele Faccio' 'Yoann Altmann'] In this work, we address the reconstruction of spatial patterns that are encoded in light fields associated with a series of light pulses emitted by a laser source and imaged using photon-counting cameras, with an intrinsic response significantly…
Yin Zhang, Yongchao Zhang, Yulin Huang, Jianyu Yang + 1 more
This paper presents a sparse superresolution approach for high cross-range resolution imaging of forward-looking scanning radar based on the Bayesian criterion. First, a novel forward-looking signal model is established as the product of the measurement matrix and the cross-range target distribution, which is more…
Ikuko Uwano, Makoto Sasaki, Kohsuke Kudo, Timothé Boutelier + 3 more
'Hiroyuki Kameda' 'Futoshi Mori' 'Fumio Yamashita'] Purpose: The Bayesian estimation algorithm improves the precision of bolus tracking perfusion imaging. However, this algorithm cannot directly calculate Tmax, the time scale widely used to identify ischemic penumbra, because Tmax is a non-physiological, artificial…
Karl J. Friston, Vladimir Litvak, Ashwini Oswal, Adeel Razi + 4 more
'Klaas E. Stephan' 'Bernadette C.M. van Wijk' 'Gabriel Ziegler' 'Peter Zeidman'] This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject)…
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)…
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…
Albert Podusenko, Wouter M. Kouw, Bert de Vries, Rafael Rumí
Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by a factor graph and solve the inference problem by automated message…
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…