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# Documents  Robert, Christian P. | enregistrements trouvés : 16

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## Bayesian modelling Mengersen, Kerrie | CIRM H

Post-edited

Research School

This tutorial will be a beginner’s introduction to Bayesian statistical modelling and analysis. Simple models and computational tools will be described, followed by a discussion about implementing these approaches in practice. A range of case studies will be presented and possible solutions proposed, followed by an open discussion about other ways that these problems could be tackled.

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## Markov Chain Monte Carlo Methods - Part 1 Robert, Christian P. | CIRM H

Post-edited

Research talks;Probability and Statistics

In this short course, we recall the basics of Markov chain Monte Carlo (Gibbs & Metropolis sampelrs) along with the most recent developments like Hamiltonian Monte Carlo, Rao-Blackwellisation, divide & conquer strategies, pseudo-marginal and other noisy versions. We also cover the specific approximate method of ABC that is currently used in many fields to handle complex models in manageable conditions, from the original motivation in population genetics to the several reinterpretations of the approach found in the recent literature. Time allowing, we will also comment on the programming developments like BUGS, STAN and Anglican that stemmed from those specific algorithms. In this short course, we recall the basics of Markov chain Monte Carlo (Gibbs & Metropolis sampelrs) along with the most recent developments like Hamiltonian Monte Carlo, Rao-Blackwellisation, divide & conquer strategies, pseudo-marginal and other noisy versions. We also cover the specific approximate method of ABC that is currently used in many fields to handle complex models in manageable conditions, from the original motivation in population ...

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## Big data for health: a Bayesian spatio-temporal analysis for predicting cardiac risk in Ticino and optimal defibrillators positioning Mira, Antonietta | CIRM H

Multi angle

Research talks

The term ‘Public Access Defibrillation’ (PAD) is referred to programs based on the placement of Automated External Defibrillators (AED) in key locations along cities’ territory together with the development of a training plan for users (first responders). PAD programs are considered necessary since time for intervention in cases of sudden cardiac arrest outside of a medical environment (out-of-hospital cardiocirculatory arrest, OHCA) is strongly limited: survival potential decreases from a 67% baseline by 7 to 10% for each minute of delay in first defibrillation. However, it is widely recognized that current PAD performance is largely below its full potential. We provide a Bayesian spatio-temporal statistical model for predidicting OHCAs. Then we construct a risk map for Ticino, adjusted for demographic covariates, that explains and forecasts the spatial distribution of OHCAs, their temporal dynamics, and how the spatial distribution changes over time. The objective is twofold: to efficiently estimate, in each area of interest, the occurrence intensity of the OHCA event and to suggest a new optimized distribution of AEDs that accounts for population exposure to the geographic risk of OHCA occurrence and that includes both displacement of current devices and installation of new ones. The term ‘Public Access Defibrillation’ (PAD) is referred to programs based on the placement of Automated External Defibrillators (AED) in key locations along cities’ territory together with the development of a training plan for users (first responders). PAD programs are considered necessary since time for intervention in cases of sudden cardiac arrest outside of a medical environment (out-of-hospital cardiocirculatory arrest, OHCA) is strongly ...

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## Bayesian capture-recapture in social justice research Corliss, David | CIRM H

Multi angle

Research talks

Capture-Recapture (RC) methodology provides a way to estimate the size of a population from multiple, independent samples. While the was developed more than a century ago to count animal populations, it has only recently become important in Data For Social Good. The large number of samples with varying amounts of intersection and developed over a period of time, so often found in Data For Social Good projects, can greatly complicate conventional RC methodology. These conditions are ideal, however, for Bayesian Capture Recapture. This presentation describes the use of Bayesian Capture Recapture to estimate populations in Data for Social Good. Examples illustrating this method include new work by the author in estimating numbers of human trafficking victims and in estimating the size of hate groups from the analysis of hate speech in social media. Capture-Recapture (RC) methodology provides a way to estimate the size of a population from multiple, independent samples. While the was developed more than a century ago to count animal populations, it has only recently become important in Data For Social Good. The large number of samples with varying amounts of intersection and developed over a period of time, so often found in Data For Social Good projects, can greatly complicate conventional ...

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## Model assessment, selection and averaging Vehtari, Aki | CIRM H

Multi angle

Research School

The tutorial covers cross-validation, and projection predictive approaches for model assessment, selection and inference after model selection and Bayesian stacking for model averaging. The talk is accompanied with R notebooks using rstanarm, bayesplot, loo, and projpred packages.

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## An introduction to particle filters Chopin, Nicolas | CIRM H

Multi angle

Research School

This course will give a gentle introduction to SMC (Sequential Monte Carlo algorithms):
• motivation: state-space (hidden Markov) models, sequential analysis of such models; non-sequential problems that may be tackled using SMC.
• Formalism: Markov kernels, Feynman-Kac distributions.
• Monte Carlo tricks: importance sampling and resampling
• standard particle filters: bootstrap, guided, auxiliary
• maximum likelihood estimation of state-stace models
• Bayesian estimation of these models: PMCMC, SMC$^2$.
This course will give a gentle introduction to SMC (Sequential Monte Carlo algorithms):
• motivation: state-space (hidden Markov) models, sequential analysis of such models; non-sequential problems that may be tackled using SMC.
• Formalism: Markov kernels, Feynman-Kac distributions.
• Monte Carlo tricks: importance sampling and resampling
• standard particle filters: bootstrap, guided, auxiliary
• maximum likelihood estimation of state-stace ...

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## Bayesian computation with INLA Rue, Havard | CIRM H

Multi angle

Research School

This talk focuses on the estimation of the distribution of unobserved nodes in large random graphs from the observation of very few edges. These graphs naturally model tournaments involving a large number of players (the nodes) where the ability to win of each player is unknown. The players are only partially observed through discrete valued scores (edges) describing the results of contests between players. In this very sparse setting, we present the first nonasymptotic risk bounds for maximum likelihood estimators (MLE) of the unknown distribution of the nodes. The proof relies on the construction of a graphical model encoding conditional dependencies that is extremely efficient to study n-regular graphs obtained using a round-robin scheduling. This graphical model allows to prove geometric loss of memory properties and deduce the asymptotic behavior of the likelihood function. Following a classical construction in learning theory, the asymptotic likelihood is used to define a measure of performance for the MLE. Risk bounds for the MLE are finally obtained by subgaussian deviation results derived from concentration inequalities for Markov chains applied to our graphical model. This talk focuses on the estimation of the distribution of unobserved nodes in large random graphs from the observation of very few edges. These graphs naturally model tournaments involving a large number of players (the nodes) where the ability to win of each player is unknown. The players are only partially observed through discrete valued scores (edges) describing the results of contests between players. In this very sparse setting, we ...

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## Bayesian computational methods Robert, Christian P. | CIRM H

Multi angle

Research School

This is a short introduction to the many directions of current research in Bayesian computational statistics, from accelerating MCMC algorithms, to using partly deterministic Markov processes like the bouncy particle and the zigzag samplers, to approximating the target or the proposal distributions in such methods. The main illustration focuses on the evaluation of normalising constants and ratios of normalising constants.

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## Mixtures: estimation and applications Mengersen, Kerrie ; Robert, Christian P. ; Titterington, D. Michael | Wiley 2011

Ouvrage

- xviii; 311 p.
ISBN 978-1-119-99389-6

Wiley series in probability and statistics

Localisation : Ouvrage RdC (MIXT)

mélange # distribution hétérogène

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## Méthodes de Monte-Carlo avec R Robert, Christian P. ; Casella, George ; Robert, Joachim ; Ryder, Robin ; Arbel, Julyan ; Jacob, Pierre ; Plessis, Brigitte | Springer 2011

Ouvrage

- xv; 256 p.
ISBN 978-2-8178-0180-3

Pratique R

Localisation : Ouvrage RdC (ROBE)

méthode de Monte-Carlo # langage de programmation R # simulation statistique # analyse bayésienne

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## The Bayesan choice :from decision-theoretic foundations to computational implementation Robert, Christian P. | Springer 2001

Ouvrage

- 602 p.
ISBN 978-0-387-71598-8

Springer texts in statistics

Localisation : Ouvrage RdC (ROBE)

statistiques # problème de Bayes # théorie de la décision en statistiques # inférence de Bayes # loi à priori # estimation ponctuelle # modèle de choix # admissibilité # invarience

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## Le choix bayésien :principes et pratique Robert, Christian P. | Springer 2006

Ouvrage

- 638 p.
ISBN 978-2-287-25173-3

Statistique et probabilités appliquées

Localisation : Ouvare RdC (ROBE)

analyse bayésienne # méthode MCML # inférence statistique

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## Monte Carlo statistical methods Robert, Christian P. ; Casella, George | Springer 2004

Ouvrage

- 645 p.
ISBN 978-0-387-21239-5

Springer texts in statistics

Localisation : Ouvrage RdC (ROBE)

méthode de simulation # méthode de Monte Carlo # génération aléatoire # chaîne de Markov # variable patente # statistique bayésienne # algorithme de Métropolis

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## Monte Carlo statistical methods Robert, Christian P. ; Casella, George | Springer 1999

Ouvrage

- 507 p.
ISBN 978-0-387-98707-1

Springer texts in statistics

Localisation : Ouvrage RdC (ROBE)

méthode de simulation # méthode de Monte Carlo # génération aléatoire # chaîne de Markov # variable patente # statistique bayésienne # algorithme de Métropolis

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## The bayesian choice :from decision-theoretic foundations to computational implementation Robert, Christian P. | Springer 2001

Ouvrage

- 604 p.
ISBN 978-0-387-95231-4

Springer texts in statistics

Localisation : Ouvrage RdC (ROBE)

statistique inférentielle # théorème de Bayes # estimation ponctuelle # théorie de la décision # test # modélisation statistique # méthode de calcul statistique # choix de modèle # phénomène de Stein # statistique bayésienne

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## Discretization and MCMC convergence assessment Robert, Christian P. | Springer 1998

Ouvrage

- 192 p.
ISBN 978-0-387-98591-6

Lecture notes in statistics , 0135

Localisation : Ouvrage RdC (Disc)

convergence # processus de markov # méthode de Monté-Carlo # analyse numérique # paramètre discret # chaîne de Markov à paramètre discret # interférence bayesienne # application à la biologie # théorème limite # discrétisation

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