4,009 Hits in 4.5 sec

Rapid Bayesian Inference of Global Network Statistics Using Random Walks

Willow B. Kion-Crosby, Alexandre V. Morozov
2018 Physical Review Letters  
We propose a novel Bayesian methodology which uses random walks for rapid inference of statistical properties of undirected networks with weighted or unweighted edges.  ...  We also infer properties of the large-scale network formed by hyperlinks between Wikipedia pages.  ...  Here we develop a Bayesian approach to network sampling by random walks (RWs) [4, 7] .  ... 
doi:10.1103/physrevlett.121.038301 pmid:30085804 fatcat:ezc3qr3ixrfmpopatia3pm4o6i

Superstatistical analysis and modelling of heterogeneous random walks

Claus Metzner, Christoph Mark, Julian Steinwachs, Lena Lautscham, Franz Stadler, Ben Fabry
2015 Nature Communications  
The timedependent statistical parameters can be extracted from measured random walk trajectories with a Bayesian method of sequential inference.  ...  In particular, random walks with time-varying statistical properties are found in many scientific disciplines.  ...  We also thank Pamela Strissel and Reiner Strick (University of Erlangen, University Clinics) for establishment of a primary inflammatory breast cancer cell line and for sharing these cells with our laboratory  ... 
doi:10.1038/ncomms8516 pmid:26108639 pmcid:PMC4491834 fatcat:gvkxu4ffenczpje3bhhsxkwt3e

Amortised inference of fractional Brownian motion with linear computational complexity [article]

Hippolyte Verdier, François Laurent, Christian Vestergaard, Jean-Baptiste Masson, Alhassan Cassé
2022 arXiv   pre-print
We introduce a simulation-based, amortised Bayesian inference scheme to infer the parameters of random walks.  ...  In the first step a graph neural network is trained on simulated data to learn optimized low-dimensional summary statistics of the random walk.  ...  We looked at the learnt summary statistics h, that after training constitutes a low-dimensional representation of the random walks which is use as features to compute the posterior distribution.  ... 
arXiv:2203.07961v2 fatcat:qz4fyeacabebljgozavzflzndm

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Yangheran Piao, Kai Ye, Xiaohui Cui
2021 IEEE Access  
With the rapid development of social networks, users pay more and more attention to the protection of personal information.  ...  Social relationship inference and attribute inference are two basic attacks on users' privacy in social networks. This is the first systematic review of privacy inference attacks in social networks.  ...  In the bipartite graph of user location, the random walk method is used to obtain the random walk traces, which represent the neighbours of each user in the mobile context.  ... 
doi:10.1109/access.2021.3064208 fatcat:rljfmzrkenfctjpcgpzrpvmume

A stimulus-free graphical probabilistic switching model for sequential circuits using dynamic bayesian networks

Sanjukta Bhanja, Karthikeyan Lingasubramanian, N. Ranganathan
2006 ACM Transactions on Design Automation of Electronic Systems  
mechanism for probabilistic inference.  ...  This model, which we refer to as the temporal dependency model (TDM), can be constructed from the logic structure and is shown to be a dynamic Bayesian Network.  ...  dynamic bayesian networks. (3) This work use a stochastic and accurate inference schemes for propagating probabilities in the dynamic Bayesian Networks. (4) Since we represent the joint probability distribution  ... 
doi:10.1145/1142980.1142990 fatcat:rzkxsjqtqzazjjrzymcakcqgki

Futuristic methods in virus genome evolution using the Third-Generation DNA sequencing and artificial neural networks [article]

Hyunjin Shim
2019 arXiv   pre-print
In particular, deep learning is a field of machine learning that is used to solve complex problems through artificial neural networks.  ...  Unlike other methods, features can be learned using neural networks entirely from data without manual specifications.  ...  Monte Carlo step which is a random walk taking a large sample, 2. Markov Chain step which estimates the expectation of the probability distribution.  ... 
arXiv:1902.09148v1 fatcat:p74hb5tvkfdcbkz4nba6gvyvca

Conference Summary: Astronomy Perspective of Astro-Statistics [article]

Ofer Lahav
2006 arXiv   pre-print
We also comment on the pros and cons of Globalization of scientific research.  ...  We describe two examples where Bayesian methods have improved our inference: (i) photometric redshift estimation (ii) orbital parameters of extra-solar planets.  ...  It is a pleasure to thank, on behalf of the participants, the Co-organizers Jogesh Babu, Eric Feigelson, the SOC (JB, EF, Jim Berger, Kris Gorski, Thomas Laredo, Vicent Martinez, Larry Wasserman, Michael  ... 
arXiv:astro-ph/0610713v1 fatcat:g7mtfrxjorap5hmnc2zh3xzbje

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Sebastian Bitzer, Stefan J. Kiebel
2012 Biological cybernetics  
We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool.  ...  Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications.  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s)  ... 
doi:10.1007/s00422-012-0490-x pmid:22581026 fatcat:y3prg6rhfjg2bivyndcydzmsoq

Bayesian recognition of safety relevant motion activities with inertial sensors and barometer

Korbinian Frank, Estefania Munoz Diaz, Patrick Robertson, Francisco Javier Fuentes Sanchez
2014 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014  
Walking, standing, sitting or lying have been detected with more or less confidence, in more or less suitable system designs.  ...  The major inconvenience is the previous installation of cameras and, in consequence, that the area to track the subject is limited. But still, video is used for instance in [3], [4] or [5].  ...  Figure 9 . 9 Bayesian Network classifier. The ellipsoids represent random variables.  ... 
doi:10.1109/plans.2014.6851373 fatcat:vptk4yinuzgwblwi6auqyutcne

Applications of Data Mining Methods in the Integrative Medical Studies of Coronary Heart Disease: Progress and Prospect

Yan Feng, Yixin Wang, Fang Guo, Hao Xu
2014 Evidence-Based Complementary and Alternative Medicine  
A large amount of studies show that real-world study has strong external validity than the traditional randomized controlled trials and can evaluate the effect of interventions in a real clinical setting  ...  Data mining techniques are to analyze and dig out useful information and knowledge from the mass data to guide people's practices.  ...  Acknowledgments The current work was partially supported by Beijing Committee of Science and Technology (no.  ... 
doi:10.1155/2014/791841 pmid:25544853 pmcid:PMC4269208 fatcat:x4nsey6agzfszkaljdwyk5siiu

Bayesian Active Learning for Drug Combinations

Mijung Park, Marcel Nassar, Haris Vikalo
2013 IEEE Transactions on Biomedical Engineering  
When computing the criterion, we marginalize out the GP hyperparameters in a fully Bayesian manner using a particle filter.  ...  We demonstrate the effectiveness of our approach on a fullyfactorial Drosophila dataset, an antiviral drug dataset for Herpes simplex virus type 1, and simulated human Apoptosis networks.  ...  a guided random walk on a discrete grid of drug doses.  ... 
doi:10.1109/tbme.2013.2272322 pmid:23846437 fatcat:nn3ovb56mjcqdkvbvpz63eqkqi

A probabilistic framework for semantic video indexing, filtering, and retrieval

H.R. Naphide, T.S. Huang
2001 IEEE transactions on multimedia  
Using probabilistic models for six site multijects, rocks, sky, snow, water-body, forestry/greenery and outdoor and using a Bayesian belief network as the multinet we demonstrate the application of this  ...  Semantic filtering and retrieval of multimedia content is crucial for efficient use of the multimedia data repositories.  ...  A Bayesian belief network [25] - [27] is a probabilistic graphical network, which specifies a probability distribution over a set of random variables, which are represented by the nodes of the network  ... 
doi:10.1109/6046.909601 fatcat:zy4464q6bzgk7cythnaacakv74

Massive parallelization boosts big Bayesian multidimensional scaling [article]

Andrew Holbrook, Philippe Lemey, Guy Baele, Simon Dellicour, Dirk Brockmann, Andrew Rambaut, Marc Suchard
2019 arXiv   pre-print
Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statistical learning.  ...  To illustrate, we employ Bayesian MDS to infer the rate at which different seasonal influenza virus subtypes use worldwide air traffic to spread around the globe.  ...  We gratefully acknowledge support from NVIDIA Corporation with the donation of parallel computing resources used for this research.  ... 
arXiv:1905.04582v2 fatcat:vnymq6dinbfi5gvfr6bx2qwbb4

Bayesian model of dynamic image stabilization in the visual system

Y. Burak, U. Rokni, M. Meister, H. Sompolinsky
2010 Proceedings of the National Academy of Sciences of the United States of America  
Thus there is considerable uncertainty whether Bayesian inference of full images is practicable at all. We begin by laying out the stochastic constraints on this process.  ...  Prior work on Bayesian inference focused on simplified problems in which the subject estimates only a single, typically static sensory variable (1-5).  ...  random walk (12) .  ... 
doi:10.1073/pnas.1006076107 pmid:20937893 pmcid:PMC2984143 fatcat:hrxqghhhxbh6xiplnn6ezdfagu

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale [article]

Atılım Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip Torr, Victor Lee (+3 others)
2019 arXiv   pre-print
To guide IC inference, we perform distributed training of a dynamic 3DCNN--LSTM architecture with a PyTorch-MPI-based framework on 1,024 32-core CPU nodes of the Cori supercomputer with a global minibatch  ...  Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.  ...  To perform efficient inference we make use of the inference compilation technique, and we train a dynamic neural network involving LSTM and 3DCNN components, with a large global minibatch size of 128k.  ... 
arXiv:1907.03382v2 fatcat:v4yy3ywsqrcr3fblgbkohvn264
« Previous Showing results 1 — 15 out of 4,009 results