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Each year more than 4,000 choose NUI Galway as their University of choice. Find out what life at NUI Galway is all about here.
About NUI Galway
About NUI Galway
Since 1845, NUI Galway has been sharing the highest quality teaching and research with Ireland and the world. Find out what makes our University so special – from our distinguished history to the latest news and campus developments.
Colleges & Schools
Colleges & Schools
NUI Galway has earned international recognition as a research-led university with a commitment to top quality teaching across a rang of key areas of expertise.
Business & Industry
Guiding Breakthrough Research at NUI Galway
We explore and facilitate commercial opportunities for the research community at NUI Galway, as well as facilitating industry partnership.
- Alumni, Friends & Supporters
At NUI Galway, we believe that the best learning takes place when you apply what you learn in a real world context. That's why many of our courses include work placements or community projects.
Dr. Milovan Krnjajic B.Sc.,M.Sc.,Ph.D.
|Applied statistical modeling; Bayesian nonparametric mixture models; Stochastic simulation; Applications in science, engineering, finance, medicine.|
Peer Reviewed Journals
|Krnjajic, M,Draper, D (2014) 'Bayesian model comparison: Log scores and DIC'. Statistics & Probability Letters, 88 :9-14. [DOI] [Details]|
|Krnjaji\'c (2011) '\it Bayesian nonparametrics [book review of \refcno 2722987]'. J. Amer. Statist. Assoc, 106 (496):1639-1641. [Details]|
|Milovan Krnjajic (2011) 'Review of the book Bayesian Nonparametrics by N. L. Hjort, C. Holmes, P. Müller, and S. Walker (eds.)'. Journal Of The American Statistical Association, 106 (496). [Details]|
|Kottas, Athanasios and Krnjaji\'c (2009) 'Bayesian semiparametric modelling in quantile regression'. Scand. J. Stat, 36 (2):297-319. [DOI] [Details]|
|Kottas, Athanasios (2009) 'Bayesian Nonparametric Modeling in Quantile Regression'. Scandinavian Journal of Statistics, 36 (2):297-319. [Details]|
|Kottas, Athanasios, Draper, David (2008) 'Parametric and Nonparametric Bayesian Model Specification:A Case Study Involving Models for Count Data'. Journal of Computational Statistics and Data Analysis, 52 (4):2110-2128. [Details]|
|Krnjaji\'c (2008) 'Parametric and nonparametric Bayesian model specification: a case study involving models for count data'. Comput. Statist. Data Anal, 52 (4):2110-2128. [DOI] [Details]|
|J. Jurka et al. (2002) 'Active Alu elements are passed primarily through paternal germlinesA Case Study Involving Models for Count Data'. Theoretical Population Biology, 61 (4):519-530. [Details]|
|Milovan Krnjajic, and David Draper (2013) Quantifying the Price of Uncertainty in Bayesian Models Proceedings of AMSA 2013, Novosibirsk, Russia [ARAN Link] [Details]|
|Draper, David (2011) Bayesian Model Specification: Some problems related to model choice and calibration Proceedings of AMSA 2011, Novosibirsk, Russia [ARAN Link] [Details]|
|L. Glascoe et al. (2007) Development and Application of a Fast-Running Toolto Characterize Shock Damage within Tunnel Structures Proceedings of the Fifth Congress on Forensic Engineering [Details]|
|A. Kottas, M. Taddy (2007) Model-based approaches to nonparametric Bayesian quantile regression Proceedings of the 2007 Joint Statistical Meetings, Seattle, WA [Details]|
|Hanks B, McDowell C, (2005) Program quality with pair programming in CS1 Proc. American Statistic Assn. (ASA) Section on Bayesian Statistical Science, Minneapolis, 2005 [ARAN Link] [Details]|
|G. Johannesson, F.K. Chow (2004) Sequential Monte-Carlo Based Framework for Dynamic Data-Driven Event Reconstructionfor Atmospheric Release ACM SIGCSE Bulletin, 2004 , pp.176-180 [Details]|
|Milovan Krnjajic & Athanasios Kottas (2006) Technical Report on BNP models for quantile regression. University of California at Santa Cruz, Santa Cruz, California, USA. [Details]|
|Draper, David (2007) Bayesian Model Specification. Technical Report, University of California at Santa Cruz, AMS2007-9. Other Item [Details]|
|Chen B., Hickling T. et al. (2007) Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements. Technical Report, LLNL. Other Item [Details]|