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Tractable Inference for Complex Stochastic Processes
[article]
2013
arXiv
pre-print
Preserved Fulltext
Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable. ...
In the case of a stochastic system, these tasks typically involve the use of a belief state- a probability distribution over the state of the process at a given point in time. ...
Acknowledgements We gratefully acknowledge Eric Bauer, Lise Getoor, and Uri Lerner for work on the software used in the experi-ments, Raya Fratkina for help with the network files, and Tim Huang for providing ...
arXiv:1301.7362v1
fatcat:r7coczhoavg7hhdlgz65gwag6i
Cascading Denoising Auto-Encoder as a Deep Directed Generative Model
[article]
2017
arXiv
pre-print
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2015 pre-print arXiv:1511.07118v1 fatcat:ed2dvmofrbc73o7tziokys2p44
These are unavoidable when recent suc-cessful directed model like VAE (Kingma &Welling, 2014) is trained on complex dataset likereal images. ...
On theone hand, can deep directed models be success-fully trained without intractable posterior infer-ence and difficult optimization of very deep neu-ral networks in inference and generative mod-els? ...
We consider a directed model with an stochastic identity mapping (simple corruption process) as an inference model and a DAE as a generative model. ...
arXiv:1511.07118v2
fatcat:ipyhvemybjb2rkbwppj3sk2q2q
The Variational Gaussian Process
[article]
2016
arXiv
pre-print
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We develop the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. ...
Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. ...
ACKNOWLEDGEMENTS We thank David Duvenaud, Alp Kucukelbir, Ryan Giordano, and the anonymous reviewers for their helpful comments. ...
arXiv:1511.06499v4
fatcat:rxtue3ahoveytj5im3ubye5vyu
Automated Variational Inference in Probabilistic Programming
[article]
2013
arXiv
pre-print
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We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. ...
This method is efficient without restrictions on the probabilistic program; it is particularly practical for distributions which are not analytically tractable, including highly structured distributions ...
Use of stochastic approximations for variational inference is also used by Carbonetto [30] . ...
arXiv:1301.1299v1
fatcat:jchutrnmkfeqbob27lk5oz3hy4
Reformulating Inference Problems Through Selective Conditioning
[article]
2013
arXiv
pre-print
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With employ the selective conditioning approach to target specific nodes in a belief network for decomposition, based on the contribution the nodes make to the tractability of stochastic simulation. ...
We review previous work on BNRAS algorithms- randomized approximation algorithms for probabilistic inference. ...
Although we cannot avoid worst-case in tractability, we can apply methods to refine an initial problem instance by removing unnecessary complexity. ...
arXiv:1303.5397v1
fatcat:2irqr3cj7vbdhfd5eh4sjuavny
Reformulating Inference Problems Through Selective Conditioning
[chapter]
1992
Uncertainty in Artificial Intelligence
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With employ the selective con· ditioning approach to target specific nodes in a belief network for decomposition, based on the contribution the nodes make to the tractability of stochastic simulation. ...
We re view previous work on BNRAS algorithms randomized approximation algorithms for probabilistic inference. ...
Although we cannot avoid worst-case in tractability, we can apply methods to refine an initial problem instance by removing unnecessary complexity. ...
doi:10.1016/b978-1-4832-8287-9.50011-6
fatcat:m6bd4hcq3rcgjfr2n4ukurfrx4
Introduction to the Issue on Stochastic Simulation and Optimization in Signal Processing
2016
IEEE Journal on Selected Topics in Signal Processing
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He has been serving as an associate editor for the IEEE TRANSACTIONS ON SIGNAL PROCESSING (2008-2011, 2015 and for the EURASIP journal on Signal Processing (2013-present). ...
Processing Society (2001-2007, 2010-present). ...
; for example, they use stochastic models to represent the data observation process and the prior knowledge available and they obtain solutions by performing statistical inference (e.g., using maximum ...
doi:10.1109/jstsp.2016.2524963
fatcat:ogalgccjjndihjf2ck6w432hbe
Page 427 of Genetics Vol. 170, Issue 1
[page]
2005
Genetics
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The second form is based on walks over complete graphs and offers numerically tractable solutions for increasing number of taxa. ...
I demonstrate the flexibility of these stochastic models to test competing ideas about HGT by examining the complexity hypothesis and find support for increased HGT of operational genes com- pared to informational ...
Inference, Prediction, and Control of Networked Epidemics
[article]
2017
arXiv
pre-print
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We then leverage the attained conditional independence property to construct tractable mechanisms for the inference and prediction of the process state, avoiding the need to use mean field approximations ...
of the stochastic epidemic process in order to control the exact dynamics of the epidemic outbreak. ...
using standard inference algorithms for graphical models (see, e.g. [17] ) to estimate the joint distributions of the process would not be tractable. ...
arXiv:1703.07409v1
fatcat:ck6rwwt6efbx7ezk2gipgkh4t4
Page 4839 of Mathematical Reviews Vol. , Issue 95h
[page]
1995
Mathematical Reviews
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We suggest a general method for establishing such results when a stochastic process in ...
(S-RIT-OP; Stockholm);
Johansson, Bjorn (S-STOC-MS; Stockholm)
On theorems of de Finetti type for continuous time stochastic processes. ...
Computational Resource Demands of a Predictive Bayesian Brain
2019
Computational Brain & Behavior
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We discuss the implications of these sobering results for the predictive processing account and propose a way to move forward. ...
Given that many forms of cognition seem to be well characterized as a form of Bayesian inference, this conjecture has great import for cognitive science. ...
for very constructive feedback. ...
doi:10.1007/s42113-019-00032-3
fatcat:vvadybxrdrc6hedkn6hn7gzvoe
Hierarchical Variational Models
[article]
2016
arXiv
pre-print
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HVMs augment a variational approximation with a prior on its parameters, which allows it to capture complex structure for both discrete and continuous latent variables. ...
Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. ...
Local expectation gradients for doubly stochastic variational inference. In Neural Information Processing Systems. Tran, D., Blei, D. M., and Airoldi, E. M. (2015). Copula variational inference. ...
arXiv:1511.02386v2
fatcat:cyu73a35fbcdlbo57pynmxkrxe
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
[article]
2015
arXiv
pre-print
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The new method leverages a recently proposed method for scaling Expectation Propagation, called stochastic Expectation Propagation. ...
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. ...
YL thanks the Schlumberger Foundation for her Faculty for the Future PhD fellowship. RET thanks EPSRC grants EP/G050821/1 and EP/L000776/1. ...
arXiv:1511.03405v1
fatcat:hm2fjijqdfh4loqnciri3p6dnu
Predictive Simulation for Design and Optimization of Complex Physical Systems
2017
Figshare
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Optimization, Design & Decision Making Application of methods for stochastic optimization have not generally carried over to complex physical systems. ...
In addition, decisions in design of complex systems are often staged, with some decisions made early in the process, and others made later when more information is available. ...
doi:10.6084/m9.figshare.5318215.v1
fatcat:fugcgoh27ngtnpbh5a2gpe4vgq
Sparse Spatio-temporal Gaussian Processes with General Likelihoods
[chapter]
2011
Lecture Notes in Computer Science
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We use expectation propagation to perform approximate inference on non-Gaussian data, and show how to incorporate sparse approximations to further reduce the computational complexity. ...
In this paper, we consider learning of spatio-temporal processes by formulating a Gaussian process model as a solution to an evolution type stochastic partial differential equation. ...
The authors would like to thank Finnish Doctoral Programme in Computational Sciences (FICS), Centre of Excellence in Computational Complex Systems Research (COSY) and Academy of Finland for financial support ...
doi:10.1007/978-3-642-21735-7_24
fatcat:ldapp247ifcf7eycvl2f2uhaem
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