About Haavard Rue Haavard Rue Program Chair, Statistics Bayesian computational statistics bayesian methodology latent Gaussian models spatial statistics Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics. Events Presented Events Nov 14 - Nov 20, 2021 The Role and Importance of Sparse Matrices in Statistics Haavard Rue, Program Chair, Statistics Nov 18, 09:50 - 10:30 KAUST Plenary Sessions Abstract The common tool for dealing with dependence in statistics, is linear dependence described through covariance or it scaled version, correlation. In this framework, a zero in the correlation matrix indicate independence, which is a very strong property and not something we would aim for in statistical models. A weaker version of linear dependence, is conditional independence. This corresponds to the inverse correlation matrix called the precision matrix. A zero in the precision matrix corresponds to conditional independence. These matrices can be very sparse although all random Aug 30 - Sep 5, 2020 The two faces of correlation Haavard Rue, Program Chair, Statistics Sep 3, 12:00 - 13:00 KAUST Statistical Modeling computing Discussing the concept of correlation and how to interpret it alone (marginally) or within a more complex environment (conditionally). This rather simple observation is the key observation behind a lot of exciting developments and connections in statistics that can be leveraged for improved computations and better motivated statistical models.
The Role and Importance of Sparse Matrices in Statistics Haavard Rue, Program Chair, Statistics Nov 18, 09:50 - 10:30 KAUST Plenary Sessions Abstract The common tool for dealing with dependence in statistics, is linear dependence described through covariance or it scaled version, correlation. In this framework, a zero in the correlation matrix indicate independence, which is a very strong property and not something we would aim for in statistical models. A weaker version of linear dependence, is conditional independence. This corresponds to the inverse correlation matrix called the precision matrix. A zero in the precision matrix corresponds to conditional independence. These matrices can be very sparse although all random
The two faces of correlation Haavard Rue, Program Chair, Statistics Sep 3, 12:00 - 13:00 KAUST Statistical Modeling computing Discussing the concept of correlation and how to interpret it alone (marginally) or within a more complex environment (conditionally). This rather simple observation is the key observation behind a lot of exciting developments and connections in statistics that can be leveraged for improved computations and better motivated statistical models.
Engage KAUST Repository KAUST Academic Portal ORCID ShareClipboard Related Sites Statistics (STAT) Applied Mathematics and Computational Science (AMCS) Conference on Mathematical Science and Applications (CMSA) Bayesian Computational Statistics and Modeling (BAYESCOMP) Related Content Articles 45 Events 2 Related Links Google Scholar Citations R-INLA webpage
