Improved Markov Chain Monte Carlo method for cryptanalysis substitution-transposition cipher

AbstractIn this paper we first investigate the use of Markov Chain Monte Carlo (MCMC) methods to attack classical ciphers. MCMC has previously been used to break simple substitution, transposition and substitution-transposition ciphers. Here, we improve the accuracy of obtained results by these algorithms and we show the performance of the algorithms using quasi random numbers such as Faure, Sobol and Niederreiter sequences.

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An Efficient Markov-Chain Monte Carlo Method for the State Space Models with Stochastic Volatility

SSRN Electronic Journal ◽

10.2139/ssrn.2340389 ◽

2013 ◽

Author(s):

Yu-Fan Huang

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Monte Carlo Method ◽

State Space ◽

Stochastic Volatility ◽

The State ◽

State Space Models

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A Birth-Death Markov Chain Monte Carlo Method to Estimate the Number of States in a State-Contingent Production Frontier Model

SSRN Electronic Journal ◽

10.2139/ssrn.2448542 ◽

2014 ◽

Author(s):

Jose Luis Preciado Arreola ◽

Andrew Johnson

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Monte Carlo Method ◽

Production Frontier ◽

Birth Death

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Significance testing of clinical data using virus dynamics models with a Markov chain Monte Carlo method: application to emergence of lamivudine-resistant hepatitis B virus

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.1999.0932 ◽

1999 ◽

Vol 266 (1436) ◽

pp. 2359-2366 ◽

Cited By ~ 8

Author(s):

N.J. Burroughs ◽

D. Pillay ◽

D. Mutimer

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Hepatitis B Virus ◽

Monte Carlo Method ◽

Hepatitis B ◽

Significance Testing ◽

Virus Dynamics ◽

B Virus ◽

Dynamics Models

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Mapping-Linked Quantitative Trait Loci Using Bayesian Analysis and Markov Chain Monte Carlo Algorithms

Genetics ◽

10.1093/genetics/146.2.735 ◽

1997 ◽

Vol 146 (2) ◽

pp. 735-743 ◽

Cited By ~ 1

Author(s):

Pekka Uimari ◽

Ina Hoeschele

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Allele Frequencies ◽

Mcmc Methods ◽

Substitution Effects ◽

Indicator Variable ◽

Trait Loci

A Bayesian method for mapping linked quantitative trait loci (QTL) using multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (MCMC) algorithms. Parameters included were allele frequencies and substitution effects for two biallelic QTL, map positions of the QTL and markers, allele frequencies of the markers, and polygenic and residual variances. Missing data were polygenic effects and multi-locus marker-QTL genotypes. Three different MCMC schemes for testing the presence of a single or two linked QTL on the chromosome were compared. The first approach includes a model indicator variable representing two unlinked QTL affecting the trait, one linked and one unlinked QTL, or both QTL linked with the markers. The second approach incorporates an indicator variable for each QTL into the model for phenotype, allowing or not allowing for a substitution effect of a QTL on phenotype, and the third approach is based on model determination by reversible jump MCMC. Methods were evaluated empirically by analyzing simulated granddaughter designs. All methods identified correctly a second, linked QTL and did not reject the one-QTL model when there was only a single QTL and no additional or an unlinked QTL.

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