Blog Archives

debunking a (minor and personal) myth

September 9, 2015
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debunking a (minor and personal) myth

For quite a while, I entertained the idea that Beta and Dirichlet proposals  were more adequate than (log-)normal random walks proposals for parameters on (0,1) and simplicia (simplices, simplexes), respectively, when running an MCMC. For instance, for p in (0,1) the value of the Markov chain at time t-1, the proposal at time t could

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debunking a (minor and personal) myth

September 9, 2015
By
debunking a (minor and personal) myth

For quite a while, I entertained the idea that Beta and Dirichlet proposals  were more adequate than (log-)normal random walks proposals for parameters on (0,1) and simplicia (simplices, simplexes), respectively, when running an MCMC. For instance, for p in (0,1) the value of the Markov chain at time t-1, the proposal at time t could

Read more »

ABC model choice via random forests [and no fire]

September 3, 2015
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ABC model choice via random forests [and no fire]

While my arXiv newspage today had a puzzling entry about modelling UFOs sightings in France, it also broadcast our revision of Reliable ABC model choice via random forests, version that we resubmitted today to Bioinformatics after a quite thorough upgrade, the most dramatic one being the realisation we could also approximate the posterior probability of

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ABC model choice via random forests [and no fire]

September 3, 2015
By
ABC model choice via random forests [and no fire]

While my arXiv newspage today had a puzzling entry about modelling UFOs sightings in France, it also broadcast our revision of Reliable ABC model choice via random forests, version that we resubmitted today to Bioinformatics after a quite thorough upgrade, the most dramatic one being the realisation we could also approximate the posterior probability of

Read more »

reaching transcendence for Gaussian mixtures

September 2, 2015
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reaching transcendence for Gaussian mixtures

“…likelihood inference is in a fundamental way more complicated than the classical method of moments.” Carlos Amendola, Mathias Drton, and Bernd Sturmfels arXived a paper this Friday on “maximum likelihood estimates for Gaussian mixtures are transcendental”. By which they mean that trying to solve the five likelihood equations for a two-component Gaussian mixture does not

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reaching transcendence for Gaussian mixtures

September 2, 2015
By
reaching transcendence for Gaussian mixtures

“…likelihood inference is in a fundamental way more complicated than the classical method of moments.” Carlos Amendola, Mathias Drton, and Bernd Sturmfels arXived a paper this Friday on “maximum likelihood estimates for Gaussian mixtures are transcendental”. By which they mean that trying to solve the five likelihood equations for a two-component Gaussian mixture does not

Read more »

likelihood-free inference in high-dimensional models

August 31, 2015
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likelihood-free inference in high-dimensional models

“…for a general linear model (GLM), a single linear function is a sufficient statistic for each associated parameter…” The recently arXived paper “Likelihood-free inference in high-dimensional models“, by Kousathanas et al. (July 2015), proposes an ABC resolution of the dimensionality curse by turning

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likelihood-free inference in high-dimensional models

August 31, 2015
By
likelihood-free inference in high-dimensional models

“…for a general linear model (GLM), a single linear function is a sufficient statistic for each associated parameter…” The recently arXived paper “Likelihood-free inference in high-dimensional models“, by Kousathanas et al. (July 2015), proposes an ABC resolution of the dimensionality curse by turning

Read more »

abcfr 0.9-3

August 26, 2015
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abcfr 0.9-3

In conjunction with our reliable ABC model choice via random forest paper, about to be resubmitted to Bioinformatics, we have contributed an R package called abcrf that produces a most likely model and its posterior probability out of an ABC reference table. In conjunction with the realisation that we could devise an approximation to the

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abcfr 0.9-3

August 26, 2015
By
abcfr 0.9-3

In conjunction with our reliable ABC model choice via random forest paper, about to be resubmitted to Bioinformatics, we have contributed an R package called abcrf that produces a most likely model and its posterior probability out of an ABC reference table. In conjunction with the realisation that we could devise an approximation to the

Read more »

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