The Synoptic Problem: On Matthew’s and Luke’s use of Mark

Abakuks, Andris

doi:10.1111/j.1467-985x.2011.01026.x

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Their variable‐length Markov chains (VLMCs) and the associated model selection tools are based, in part, on Rissanen's tree sources and his context algorithm. The VLMC approach has been successful in applications (Busch et al, 2009; Mächler & Bühlmann, 2004) that include DNA modelling (Ben‐Gal et al, 2005; Browning, 2006) and linguistics (Abakuks, 2012; Galves et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series

Kontoyiannis

Mertzanis

Panotopoulou

et al. 2022

Journal of the Royal Statistical Society Series B: Statistical Methodology

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We develop a new Bayesian modelling framework for the class of higher‐order, variable‐memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting alg‐orithm can compute the prior predictive likelihood exa‐ctly (averaged over both models and parameters), and two related algorithms are introduced, which identify the a posteriori most likely models and compute their exact posterior probabilities. All three algorithms are deterministic and have linear‐time complexity. A family of variable‐dimension Markov chain Monte Carlo samplers is also provided, facilitating further exploration of the posterior. The performance of the proposed methods in model selection, Markov order estimation and prediction is illustrated through simulation experiments and real‐world applications with data from finance, genetics, neuroscience and animal communication. The associated algorithms are implemented in the R package BCT.

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Section: Introductionmentioning

confidence: 99%

Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series

Kontoyiannis

Mertzanis

Panotopoulou

et al. 2022

Journal of the Royal Statistical Society Series B: Statistical Methodology

View full text Add to dashboard Cite

show abstract

“…Their variable-length Markov chains (VLMCs) and the associated model selection tools are based, in part, on Rissanen's tree sources and his context algorithm. The VLMC approach has been successful in applications (Mächler and Bühlmann, 2004;Busch et al, 2009) that include DNA modelling (Ben-Gal et al, 2005;Browning, 2006) and linguistics (Galves et al, 2012;Abakuks, 2012).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Context Trees: Modelling and exact inference for discrete time series

Kontoyiannis¹,

Mertzanis²,

Panotopoulou³

et al. 2020

Preprint

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We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting algorithm can compute the prior predictive likelihood exactly (averaged over both models and parameters), and two related algorithms are introduced, which identify the a posteriori most likely models and compute their exact posterior probabilities. All three algorithms are deterministic and have linear-time complexity. A family of variable-dimension Markov chain Monte Carlo samplers is also provided, facilitating further exploration of the posterior. The performance of the proposed methods in model selection, Markov order estimation and prediction is illustrated through simulation experiments and real-world applications with data from finance, genetics, neuroscience, and animal communication.

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“…A classic example of authorship attribution comes from Mosteller and Wallace (1963) who used grammatical features such as function word frequencies and word length distributions to determine whether 12 disputed essays in The Federalist Papers were written by James Madison or Alexander Hamilton. Other typical examples include Thisted and Efron (1987), where an alleged newly discovered Shakespeare poem was tested for authenticity, and Abakuks (2012) who used statistical techniques to investigate the relationship between the various authors of the Synoptic Gospels. Reviews of the related stylometry literature can be found in Juola (2006), Holmes (1985) and Peng and Hengartner (2002).…”

Section: Introductionmentioning

confidence: 99%

Tracking the Evolution of Literary Style via Dirichlet–Multinomial Change Point Regression

Ross

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary It is typical in stylometry to assume that authors have a unique writing style which is common to all their published writings and is constant over time. Based on this assumption, statistical techniques can be used to answer literary questions, such as authorship attribution, in a quantitative manner. However, the claim that authors have a constant literary style has not received much investigation or validation. We propose a collection of statistical models based on Dirichlet–multinomial change point regression which can capture the evolution of writing style over time, including both gradual changes in style as the author matures, and abrupt changes which can be caused by extreme events in the author's life. To illustrate our framework, we study the literary output of the celebrated British author Sir Terry Pratchett, who was tragically diagnosed with Alzheimer's disease during the last years of his life. Contrary to the usual assumptions made in stylometry, we find evidence of both gradual changes in style over his lifetime, and an abrupt change which corresponds to his Alzheimer's diagnosis. We also investigate the published writings of Agatha Christie, who is also rumoured to have suffered from Alheizmer's disease towards the end of her life, and find evidence of gradual drift, but no corresponding abrupt change. The implications for stylometry and authorship attribution are discussed.

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The Synoptic Problem: On Matthew’s and Luke’s use of Mark

Cited by 4 publications

References 26 publications

Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series

Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series

Bayesian Context Trees: Modelling and exact inference for discrete time series

Tracking the Evolution of Literary Style via Dirichlet–Multinomial Change Point Regression

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