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Les fictions de l’interprétation mettent en scène des personnages confrontés à une réalité mystérieuse qu’ils tentent de comprendre, d’interpréter ou d’élucider. La réalité énigmatique peut prendre des formes diverses　: objet étrange, phénomène inexpliqué, oracle sibyllin, dont la présence est exhibée mais dont la signification est dérobée. Face à ces signes obliques ou opaques qui les déroutent et qui confèrent au récit sa dimension d’énigme, implicite ou explicite, les personnages sont autant de figures d’herméneutes qui s’efforcent d’en percer...

The Academie Royale assembled nearly all of the important French artists working at the time, maintained a virtual monopoly on teaching and exhibitions, enjoyed a priority in obtaining royal commissions, and...

In a series of thirty-five long poems, the masterful French writer here rejects the "real" world, and immerses the reader in a series of images taken from ancient mythology and fantasy.

Christian Souchon has rendered his brother's verse into a marvelous series of images, presented on flanking pages in their outstanding French originals along with marvelously evocative English-language translations. Illustrated throughout.

MICHEL GALIANA (1933-1999) wrote novels, poetry, essays, and criticism. His brother, Christian Souchon, is a translator with the...

Stochastic Dynamics for Systems Biology is one of the first books to provide a systematic study of the many stochastic models used in systems biology. The book shows how the mathematical models are used as technical tools for simulating biological processes and how the models lead to conceptual insights on the functioning of the cellular processing system. Most of the text should be accessible to scientists with basic knowledge in calculus and probability theory.

The authors illustrate the relevant Markov chain theory using realistic models from systems...

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications.

Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and...

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications.

Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and...