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SUMMARY:BSU Seminar: &quot\;Graphical and summary diagnostics for node lev
 el adequacy in Bayesian hierarchical models&quot\; - Ida Scheel\, Universi
 ty of Oslo
DTSTART:20241022T130000Z
DTEND:20241022T140000Z
UID:TALK220672@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:For Bayesian hierarchical models represented by directed acycl
 ic graphs\, all neighbours of a node provide\, potentially conflicting\, i
 nformation on the node. Hence identifying potential conflict between these
  different information sources can be important for assessing the adequacy
  of Bayesian hierarchical models. Building on this idea originating from [
 1]\, [3] constructed node based conflict measures that have been shown to 
 be well calibrated. Sharing the idea of considering potential conflict bet
 ween separate information sources for each node\, [4] constructed a graphi
 cal diagnostic lcp. This can be used both to identify conflict at each nod
 e\, and provide insight into the nature of the conflict\, hence being more
  informative than summary diagnostics. We link these two ideas together by
  constructing a new diagnostic plot iic-lcp that is supplementary to lcp\,
  but builds on the framework of [3]. It has the advantage over the lcp wit
 h the possibility to display curves corresponding to different parameters 
 in the same plot\, saving space and easing comparisons\, particularly usef
 ul for sets of parameters representing exchangeable groups. We show how to
  visually read some of the conflict measures defined in [3] from iic-lcp\,
  combining graphical and summary diagnostics.\n\nFor Bayesian hierarchical
  models represented by directed acyclic graphs\, all neighbours of a node 
 provide\, potentially conflicting\, information on the node. Hence identif
 ying potential conflict between these different information sources can be
  important for assessing the adequacy of Bayesian hierarchical models. Bui
 lding on this idea originating from [1]\, [3] constructed node based confl
 ict measures that have been shown to be well calibrated. Sharing the idea 
 of considering potential conflict between separate information sources for
  each node\, [4] constructed a graphical diagnostic lcp. This can be used 
 both to identify conflict at each node\, and provide insight into the natu
 re of the conflict\, hence being more informative than summary diagnostics
 . We link these two ideas together by constructing a new diagnostic plot i
 ic-lcp that is supplementary to lcp\, but builds on the framework of [3]. 
 It has the advantage over the lcp with the possibility to display curves c
 orresponding to different parameters in the same plot\, saving space and e
 asing comparisons\, particularly useful for sets of parameters representin
 g exchangeable groups. We show how to visually read some of the conflict m
 easures defined in [3] from iic-lcp\, combining graphical and summary diag
 nostics.\n\n[1] O'Hagan\, A. (2003). HSSS model criticism. In Green\, P. J
 .\, Richardson\, S.\, and Hjort\, N. L. (eds.)\, Highly Structured Stochas
 tic Systems\, 423–444. Oxford: Oxford University Press.\n[2] Dahl\, F. A
 .\, G  Gasemyr\, J.\, and Natvig\, B. (2007). A robust conflict measure of
  inconsistencies in Bayesian hierarchical models. Scand. J. Stat.\, 34: 81
 6–828.\n[3] Gasemyr\, J. and Natvig\, B. (2009). Extensions of a conflic
 t measure of inconsistencies in Bayesian hierarchical models. Scand. J. St
 at.\, 36: 822–838.\n[4] Scheel\, I.\, Green\, P. J.\, and Rougier\, J. C
 . (2011). A graphical diagnostic for identifying influential model Choices
  in Bayesian hierarchical models. Scand. J. Stat.\, 38: 529–550.
LOCATION:MRC Biostatistics Unit\, East Forvie Building\, Forvie Site Robin
 son Way Cambridge CB2 0SR.
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