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Registration

Registration will be on Wednesday from 7:30am to 1:30pm and on Thursday from 8:00am to 10:00am in front of the lecture hall in the building KOL, floor F, room 101.

Pictures

Program and Presentations

For downloading the presentations of the invited speakers, please click on the name in the schedule. To get contributed presentations, please click on the title.

	Wednesday 2.2.2011	Thursday 3.2.2011	Friday 4.2.2011	Saturday 5.2.2011
	Room KOL-F-101	Room KOL-F-101	Room KOL-F-101	Room SOC-1-106
08:30		Invited 2:
09:00	Tutorial INLA	G. García-Donato/	City tour	Contributed 7:
09:30	(PDF part I)	S. Frühwirth-Schnatter		Model choice II
10:00	Break	Break	Break	Break
10:30		Contributed 3:	Contributed 5:	Contributed 8:
11:00	Tutorial INLA	Latent Gaussian	Model choice I	Spatio-temporal
11:30	(PDF, part II)	models		models
12:00
12:30	Lunch	Lunch	Lunch	Lunch
13:00
13:30	Opening	Invited 3:	Invited 4:	Invited 5:
14:00	Invited 1:	F. Lindgren/	H. Rue/	C.P. Robert/
14:30	S. Sain	C. Paciorek	C. Holmes	A. Gelfand
15:00	Break	Break	Break	Closing
15:30	Contributed 1:	Contributed 4:	Contributed 6:
16:00	State-space models I	Applications I	Applications II
16:30	Break
17:00	Contributed 2:
17:30	State-space models II		Poster
18:00 - open end		Dinner	(KOL Lichthof Süd)

Contributed Sessions:

Contributed 1: State-space models I

A. Bhattacharya & S. Wilson: Towards fast recursive Bayesian inference for dynamic state-space model parameters

J. Wyse, N. Friel & H. Rue: Approximate inference for changepoint models with dependence within segments

G. Fiorentini, C. Planas & A. Rossi: Marginal likelihood for state space models using integration of scale parameters

Contributed 2: State-space models II

A.G. Wilson & Z. Ghahramani: Latent Gaussian process models

W. McCausland: The HESSIAN method (Highly efficient simulation smoothing, in a nutshell)

I. Murray & R.P. Adams: Easy, effective Monte Carlo methods for exploring latent Gaussian models

Contributed 3: Latent Gaussian models

E. Waldmann & T. Kneib: Bayesian additive quantile regression

C. Joshi & S. Wilson: GaMBA: For fast Bayesian inference on stochastic differential equation models

A. Riebler, L. Held & H. Rue: Gaussian Kronecker product Markov random fields

S. Sørbye & H. Rue: Simultaneous credible bands for latent Gaussian models

Contributed 4: Applications I

S. Rumisha, D. Gosoniu & P. Vounatsou: Accelerated failure time models for large geostatistical mortality data in Africa

A. Lau & P. McSharry: Spatio-temporal wind power probabilistic forecasts using latent Gaussian processes

B. Hrafnkelsson, J. Morris & V. Baladandayuthapani: Spatial modeling of annual minimum and maximum temperature

M. Short, P. Kronberg & D.Higdon: Estimation of Faraday rotation measures in and near the Milky Way Galaxy using a nonstationary Gaussian process model

Contributed 5: Model choice I

M. Roos & L. Held: Sensitivity analysis in Bayesian generalized linear mixed models for binary data

J. Sweeney & J. Haslett: Bayesian residual analysis for non-Gaussian response models

U. Paquet, M. Opper & O. Winther: Edgeworth expansions for improved inference in Gaussian latent variable models

A. Lenkoski, A. Dobra & A. Rodriguez: Bayesian inference for general Gaussian graphical models with application to multivariate lattice data

Contributed 6: Applications II

C. Larssen, I. Steinsland, H. Jensen & A. Roulin: Modeling and inference for Bayesian animal models in the presence of non-ignorable missing data

V. Garreta & J. Haslett: Large scale mapping of species abundance from presence-only data: Application to mammoth over millennia

S. Martino, G. Gorjanc & I. Steinsland: Application of integrated nested Laplace approximation to survival models as used in animal breeding

J. van de Kassteele, J. van Eijkeren & J. Wallinga: A recipe for describing age specific social contact patterns stratified by sex and location

Contributed 7: Model choice II

P. Lambert: Additive models for location and scale from interval censored data

D. Sabanés Bové, L. Held & G. Kauermann: Transformation and selection of covariates using a hyper-g prior for spline models

P.E. Verde: Model checking and criticisms in multivariate random-effects meta-analysis by using scale-mixtures distributions: a Bayesian approach

Contributed 8: Spatio-temporal models

V.J. Schmid: Adaptive spatio-temporal modelling for medical imaging

F. Sigrist, H.R. Künsch & W.A. Stahel: A Bayesian space-time model for rainfall

J.B. Illian & D. Simpson: Complex point processes without grids or pain