Vienna<-R 2017/V

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Jonathan Steinhart ( Bayesian Time Series Analysis with R-INLA

We will demonstrate the use of structured additive regression to
simultaneously model multiple, irregularly-spaced time series. This is achieved with the R-INLA package (, which allows fast and flexible approximate Bayesian inference using integrated nested Laplace approximations. We will begin a brief primer on the Bayesian approach and the reason for choosing INLA over more traditional approaches like Markov Chain Monte Carlo or Gaussian process regression, and will then work through the analysis of real time series from a medical domain.

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