Results 31 to 40 of about 800 (115)

Inversion of shallow seabed structure and geoacoustic parameters with waveguide characteristic impedance based on Bayesian approach

Frontiers in Marine Science, 2023
Underwater acoustic technology is essential for ocean observation, exploration and exploitation, and its development is based on an accurate predication of underwater acoustic wave propagation.
Hanhao Zhu, Hanhao Zhu, Yangyang Xue, Qunyan Ren, Qunyan Ren, Xu Liu, Jiahui Wang, Zhiqiang Cui, Shu Zhang, Huili Fan   +9 more
doaj   +1 more source

Inversion of Bayesian networks

International Journal of Approximate Reasoning
Variational autoencoders and Helmholtz machines use a recognition network (encoder) to approximate the posterior distribution of a generative model (decoder). In this paper we study the necessary and sufficient properties of a recognition network so that it can model the true posterior distribution exactly.
Jesse van Oostrum, Peter van Hintum, Nihat Ay   +2 more
openaire   +3 more sources

InvertypeR: Bayesian inversion genotyping with Strand-seq data

BMC Genomics, 2021
Background Single cell Strand-seq is a unique tool for the discovery and phasing of genomic inversions. Conventional methods to discover inversions with Strand-seq data are blind to known inversion locations, limiting their statistical power for the ...
Vincent C. T. Hanlon, Carl-Adam Mattsson, Diana C. J. Spierings, Victor Guryev, Peter M. Lansdorp   +4 more
doaj   +1 more source

Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis [PDF]

International Journal of Mining and Geo-Engineering, 2015
Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of ...
Moslem Moradi, Omid Asghari, Gholamhossein Norouzi, Mohammad Riahi, Reza Sokooti   +4 more
doaj  

Bayesian Multitask Inverse Reinforcement Learning [PDF]

, 2012
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is to formalise the problem as statistical preference elicitation, via a number of structured priors,
Christos Dimitrakakis, Constantin A. Rothkopf   +1 more
openaire   +2 more sources

A VTI anisotropic media inversion method based on the exact reflection coefficient equation

Frontiers in Physics, 2022
Anisotropy is widespread in the Earth’s crust, and VTI (vertical axis symmetry transverse isotropy) anisotropy is common due to stratigraphic pressure. Disregarding anisotropy leads to inaccurate inversion results in VTI media.
Fan Wu, Fan Wu, Fan Wu, Jingye Li, Jingye Li, Jingye Li, Weiheng Geng, Weiheng Geng, Weiheng Geng, Wei Tang, Wei Tang, Wei Tang   +11 more
doaj   +1 more source

Perspectives on Geoacoustic Inversion of Ocean Bottom Reflectivity Data

Journal of Marine Science and Engineering, 2016
This paper focuses on acoustic reflectivity of the ocean bottom, and describes inversion of reflection data from an experiment designed to study the physical properties and structure of the ocean bottom.
N. Ross Chapman
doaj   +1 more source

Asteroid Photometric Phase Functions From Bayesian Lightcurve Inversion

Frontiers in Astronomy and Space Sciences, 2022
Photometry is an important tool for characterizing the physical properties of asteroids. An asteroid’s photometric lightcurve and phase curve refer to the variation of the asteroid’s disk-integrated brightness in time and in phase angle (the Sun-asteroid-
Karri Muinonen, Karri Muinonen, Elizaveta Uvarova, Julia Martikainen, Antti Penttilä, Alberto Cellino, Xiaobin Wang, Xiaobin Wang   +7 more
doaj   +1 more source

The Effect of Available Data on the Worth of Future Observations for Groundwater Modeling

Water Resources Research
Groundwater model parameters need to be inferred on the basis of limited observation data, resulting in prediction uncertainty. The reduction of this uncertainty via future complementary observations is of high importance for many problems and can be ...
Max G. Rudolph, Thomas Wöhling, Thorsten Wagener, Andreas Hartmann   +3 more
doaj   +1 more source

Scalable Bayesian Inverse Reinforcement Learning

CoRR, 2021
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular setting due to the need for an inner-loop MDP solver, and even non-Bayesian methods that do themselves scale often require extensive ...
Alex James Chan, Mihaela van der Schaar
openaire   +3 more sources