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Causal Inference by Stochastic Complexity
[article]

2017
*
arXiv
*
pre-print

Due to

arXiv:1702.06776v1
fatcat:ngerxllnaffmdil5zupcohugmy
*the*halting problem, however, this notion is not*computable*. We hence propose to do causal inference by*stochastic**complexity*. ... We instantiate this framework, which we call CISC,*for*pairs of univariate discrete variables, using*the*class of*multinomial*distributions. ...*For*this class*the**stochastic**complexity*is*computable*remarkably e ciently, by which our score has only*a**linear*-*time**computational**complexity*. rough experiments we show that our method, ,*for*causal ...##
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Augment and Reduce: Stochastic Inference for Large Categorical Distributions
[article]

2018
*
arXiv
*
pre-print

To address this problem, we propose augment and reduce (

arXiv:1802.04220v3
fatcat:nopyq4p4znfbfkd4syeqp2paiu
*A*&R),*a*method to alleviate*the**computational**complexity*. ...*A*&R uses two ideas: latent variable augmentation and*stochastic*variational inference. It maximizes*a*lower bound on*the*marginal likelihood of*the*data. ... Ruiz is supported by*the*EU Horizon 2020 programme (Marie Skłodowska-Curie Individual Fellowship, grant agreement 706760). We also thank Victor Elvira and Pablo Moreno*for*their comments and help. ...##
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On recurrence formulas for computing the stochastic complexity

2008
*
2008 International Symposium on Information Theory and Its Applications
*

Utilizing this framework, we derive

doi:10.1109/isita.2008.4895423
fatcat:p7yf7qvzgzbjncxythdl66dite
*a*new recurrence relation over*the*values of*a**multinomial*variable, and show how to apply*the*recurrence*for**computing**the**stochastic**complexity*. ... There now exists new efficient*computation*methods, based on generating functions,*for**computing**the**stochastic**complexity*in*the**multinomial*case. ... This work was supported in part by*the*Academy of Finland under*the*project Civi and by*the*Finnish Funding Agency*for*Technology and Innovation under*the*projects Kukot and PMMA. ...##
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Image similarity: from syntax to weak semantics

2010
*
Multimedia tools and applications
*

Myllymäki,

doi:10.1007/s11042-010-0562-7
fatcat:gkj2f54acvg27jv55vyqydjypi
*A**linear*-*time**algorithm**for**computing**the**multinomial**stochastic**complexity*. Information Processing Letters 103 (2007) 6 (September), 227-233. 3.9. ... Myllymäki,*Computing**the**Multinomial**Stochastic**Complexity*in Sub-*Linear**Time*. Pp. 209-216 in Proceedings of*the*4th European Workshop on Probabilistic Graphical Models (PGM-08), edited by M. ...##
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NML Computation Algorithms for Tree-Structured Multinomial Bayesian Networks

2007
*
EURASIP Journal on Bioinformatics and Systems Biology
*

In this paper, we first review some existing

doi:10.1155/2007/90947
pmid:18382603
pmcid:PMC3171356
fatcat:nz2acyrz4fce3oisxm2hwdyj5u
*algorithms**for*efficient NML*computation*in*the*case of*multinomial*and naive Bayes model families. ... In*the*case of discrete data, straightforward*computation*of*the*NML distribution requires exponential*time*with respect to*the*sample size, since*the*definition involves*a*sum over all*the*possible data ... ACKNOWLEDGMENTS*The*authors would like to thank*the*anonymous reviewers and Jorma Rissanen*for*useful comments. ...##
###
Page 1693 of Mathematical Reviews Vol. , Issue 95c
[page]

1995
*
Mathematical Reviews
*

*A*two-stage

*computational*(2-point exchange and Newton-type)

*algorithm*

*for*finding

*the*optimal Latin hypercube design is pre- sented. ... Summary: “

*A*family of one-dimensional

*linear*

*stochastic*approx- imation procedures in continuous

*time*, where

*the*error processes are Gaussian martingales, is considered. ...

##
###
Page 3376 of Mathematical Reviews Vol. , Issue 92f
[page]

1992
*
Mathematical Reviews
*

*For*sparse polynomials

*the*Simp

*algorithm*multiplies using

*a*simple divide and conquer approach, and

*the*NOMC

*algorithm*

*computes*powers using

*a*

*multinomial*expansion. ... By replacing

*the*universal

*computer*by

*a*class of probabilistic models

*the*author modifies

*the*

*algorithmic*notion of

*complexity*and calls

*the*new measure

*the*

*stochastic*

*complexity*of

*the*data, relative ...

##
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Editorial

2003
*
Computational Statistics & Data Analysis
*

*The*proposed

*algorithm*is used to

*compute*

*the*estimator of

*a*

*time*-series model. ...

*A*learning

*algorithm*

*for*optimizing

*time*-and frequency-resolution pursuits is described. Murat K. ...

##
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On Families of New Adaptive Compression Algorithms Suitable for Time-Varying Source Data
[chapter]

2004
*
Lecture Notes in Computer Science
*

As opposed to higher-order statistical models, our schemes require

doi:10.1007/978-3-540-30198-1_24
fatcat:dketyqfs4beixl6wk3hc2hw33u
*linear*space*complexity*, and compress with nearly 10% better efficiency than*the*traditional adaptive coding methods. ... In this paper, we introduce*a*new approach to adaptive coding which utilizes*Stochastic*Learning-based Weak Estimation (SLWE) techniques to adaptively update*the*probabilities of*the*source symbols. ...*The**algorithm**for*updating*the*probabilities by using*a*nonlinear SLWE scheme is similar to*the**linear*case, except that*the*updating rule is changed to be that of (3) and (4) in*Algorithm*Probability ...##
###
Deterministic particle filters for joint blind equalization and decoding on frequency selective channels

2005
*
IEEE/SP 13th Workshop on Statistical Signal Processing, 2005
*

Numerical simulations show that

doi:10.1109/ssp.2005.1628624
fatcat:3zkgrdr36nbchl7alo6ptmbdzq
*the**algorithm*employing deterministic particle selection greatly outperforms alternative*stochastic*strategies, even when*the*latter employ*the*optimal importance function ... This work proposes deterministic particle filtering structures*for*joint blindly equalizing/decoding convolutionally coded signals transmitted over frequency selective channels. ... PARTICLE FILTERS Let y n denote*the*observed output at instant n of*a*possibly non-*linear*and*time*-varying*stochastically*driven system whose state variable x n we want to estimate. ...##
###
Manifold Optimization Over the Set of Doubly Stochastic Matrices: A Second-Order Geometry
[article]

2018
*
arXiv
*
pre-print

*The*manifolds, called

*the*doubly

*stochastic*, symmetric and

*the*definite

*multinomial*manifolds, generalize

*the*simplex also known as

*the*

*multinomial*manifold. ... On

*the*other hand, optimization

*algorithms*on manifold have shown great ability in finding solutions to nonconvex problems in reasonable

*time*. ... TABLE I

*COMPLEXITY*I OF

*THE*STEEPEST DESCENT AND NEWTON'S METHOD

*ALGORITHMS*

*FOR*

*THE*PROPOSED MANIFOLDS. ...

##
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Fast Parallel Algorithms for Edge-Switching to Achieve a Target Visit Rate in Heterogeneous Graphs

2014
*
2014 43rd International Conference on Parallel Processing
*

One of

doi:10.1109/icpp.2014.15
dblp:conf/icpp/BhuiyanCKM14
fatcat:mq7tdop57vbrjertkn5p73vpuu
*the*steps in our edge switch*algorithm*requires*the**computation*of*multinomial*random variables in parallel.*The*paper presents*the*first non-trivial parallel*algorithm**for**the*problem. ...*The*growth of real-world networks motivates*the*need to develop efficient parallel*algorithms**for*performing*a*large sequence of edge switch operations. ... Parallel*Algorithm**for**Computing**Multinomial*Distribution Based on*the*conditional distributed method shown in*Algorithm*4, we propose*a*parallel*algorithm**for**computing**multinomial*distribution X ∼ M ...##
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Driven-dissipative quantum Monte Carlo method for open quantum systems

2018
*
Physical Review A
*

*The*method enables

*stochastic*sampling of

*the*Liouville-von-Neumann

*time*evolution of

*the*density matrix, thanks to

*a*massively parallel

*algorithm*, thus providing estimates of observables on

*the*non-equilibrium ... We develop

*a*real-

*time*Full Configuration Interaction Quantum Monte Carlo approach

*for*

*the*modeling of driven-dissipative open quantum systems. ... We are indebted to Hugo Flayac

*for*having provided

*the*MCWF simulations used to benchmark

*the*present results. ...

##
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DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
[article]

2018
*
arXiv
*
pre-print

In this paper, we present

arXiv:1604.04706v7
fatcat:xxcezmmqwngd3bkcvdwnao6avq
*a*distributed*stochastic*gradient descent based optimization method (DS-MLR)*for*scaling up*multinomial*logistic regression problems to massive scale datasets without hitting ... This is primarily because one needs to*compute**the*log-partition function on every data point. This makes distributing*the**computation*hard. ... This is*a*proxy*for**the*precision@k curve and gives*a*more closer indication of*the*predictive performance of*a**multinomial*classification*algorithm*. ...##
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Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

2022
*
Mathematical Problems in Engineering
*

*For*

*the*considered benchmark datasets,

*the*

*linear*SVC has outperformed other classifiers overall when prominent features are selected. ...

*The*overall classification evaluation results are compared using different classifiers such as

*multinomial*Naive Bayes (MNB), Bernoulli Naive Bayes (BNB),

*Stochastic*Gradient Descent (SGD), Support Vector ... Acknowledgments is research was supported by

*the*Researchers Supporting Project number (RSP-2021/244), King Saud University, Riyadh, Saudi Arabia. ...

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