I am starting to get a knowledge about the Bayesian methods in high dimensional settings. The following two references are what I am getting at.
Hjort, Nils Lid. "Bayesian approaches to non-and semiparametric density estimation." Preprint series. Statistical Research Report http://urn. nb. no/URN: NBN: no-23420 (1994).
Müller, Peter, and Fernando A. Quintana. "Nonparametric Bayesian data analysis." Statistical science (2004): 95-110.
Interestingly enough, most Bayesian nonparametrics/parametrics reference did not mention high dimensional setting in full details. The only work that I know of is Chap.9 of :
Ghosh, Jayanta K., Mohan Delampady, and Tapas Samanta. An introduction to Bayesian analysis: theory and methods. Springer Science & Business Media, 2007.
This is a nice short chapter with emphasis in classical hypothesis testing. However, its material is obviously a bit out-dated given the research progress in this field. A book-length treatment assuming a relatively high-level of pre-knowledge is
Frigessi, Arnoldo, et al., eds. Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014. Vol. 11. Springer, 2016.
And most high-dimensional related introduction did not give enough time to Bayesian modelling in high dimensional setting.
My question is: Are there any reference/introductory paper that allows readers to grasp a panoramic view of Bayesian procedures in high dimensional settings? Preferably it should be readable and gives a big picture instead of falling into too much technicalities.