biblio.bib

%% Bib-file example
%
% use \url{url} for web-page addresses in all other entrytypes except url (see examples)
%
% Entrytype keywords is used to separate different entries for biblatex, myown is for your own publications
%
@Book{Scutari2013,
title = {Bayesian Networks in {R} with Applications in Systems
  Biology},
author = {Radhakrishnan Nagarajan and Marco Scutari},
publisher = {Springer},
address = {New York},
year = {2013},
note = {ISBN 978-1-4614-6445-7, 978-1-4614-6446-4},
doi = {10.1007/978-1-4614-6446-4},
}

@Article{Zeevi2015,
author={Zeevi, David
and Korem, Tal
and Zmora, Niv
and Israeli, David
and Rothschild, Daphna
and Weinberger, Adina
and Ben-Yacov, Orly
and Lador, Dar
and Avnit-Sagi, Tali
and Lotan-Pompan, Maya
and Suez, Jotham
and Mahdi, Jemal Ali
and Matot, Elad
and Malka, Gal
and Kosower, Noa
and Rein, Michal
and Zilberman-Schapira, Gili
and Dohnalov{\'a}, Lenka
and Pevsner-Fischer, Meirav
and Bikovsky, Rony
and Halpern, Zamir
and Elinav, Eran
and Segal, Eran},
title={Personalized Nutrition by Prediction of Glycemic Responses},
journal={Cell},
year={2015},
month={November},
publisher={Elsevier},
volume={163},
number={5},
pages={1079-1094},
issn={0092-8674},
doi={10.1016/j.cell.2015.11.001},
url={http://dx.doi.org/10.1016/j.cell.2015.11.001}
}

@article{dawid1993,
author = "Dawid, A. P. and Lauritzen, S. L.",
doi = "10.1214/aos/1176349260",
fjournal = "The Annals of Statistics",
journal = "Ann. Statist.",
month = "09",
number = "3",
pages = "1272--1317",
publisher = "The Institute of Mathematical Statistics",
title = "Hyper Markov Laws in the Statistical Analysis of Decomposable Graphical Models",
url = "https://doi.org/10.1214/aos/1176349260",
volume = "21",
year = "1993"
}

@book{Koller:2009:PGM:1795555,
 keywords = {other},
 author = {Koller, Daphne and Friedman, Nir},
 title = {Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning},
 year = {2009},
 isbn = {0262013193, 9780262013192},
 publisher = {The MIT Press},
} 

@Book{Scutari2014,
title = {Bayesian Networks with Examples in {R}},
author = {Marco Scutari and Jean-Baptiste Denis},
publisher = {Chapman and Hall},
address = {Boca Raton},
year = {2014},
note = {ISBN 978-1-4822-2558-7, 978-1-4822-2560-0},
}
  
@book{Bishop:2006:PRM:1162264,
 keywords = {other},
 author = {Bishop, Christopher M.},
 title = {Pattern Recognition and Machine Learning (Information Science and Statistics)},
 year = {2006},
 isbn = {0387310738},
 publisher = {Springer-Verlag New York, Inc.},
 address = {Secaucus, NJ, USA},
} 

@Article{Aussem2010,
author="Aussem, Alex
and Tchernof, Andr{\'e}
and de Morais, S{\'e}rgio Rodrigues
and Rome, Sophie",
title="Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks",
journal="BMC Bioinformatics",
year="2010",
month="Sep",
day="28",
volume="11",
number="1",
pages="487",
abstract="The aim of this study was to provide a framework for the analysis of visceral obesity and its determinants in women, where complex inter-relationships are observed among lifestyle, nutritional and metabolic predictors. Thirty-four predictors related to lifestyle, adiposity, body fat distribution, blood lipids and adipocyte sizes have been considered as potential correlates of visceral obesity in women. To properly address the difficulties in managing such interactions given our limited sample of 150 women, bootstrapped Bayesian networks were constructed based on novel constraint-based learning methods that appeared recently in the statistical learning community. Statistical significance of edge strengths was evaluated and the less reliable edges were pruned to increase the network robustness. To allow accessible interpretation and integrate biological knowledge into the final network, several undirected edges were afterwards directed with physiological expertise according to relevant literature.",
issn="1471-2105",
doi="10.1186/1471-2105-11-487",
url="http://dx.doi.org/10.1186/1471-2105-11-487"
}

@article{dawid1993,
author = "Dawid, A. P. and Lauritzen, S. L.",
doi = "10.1214/aos/1176349260",
fjournal = "The Annals of Statistics",
journal = "Ann. Statist.",
month = "09",
number = "3",
pages = "1272--1317",
publisher = "The Institute of Mathematical Statistics",
title = "Hyper Markov Laws in the Statistical Analysis of Decomposable Graphical Models",
url = "http://dx.doi.org/10.1214/aos/1176349260",
volume = "21",
year = "1993"
}

@Article{Bae2016,
author={Bae, Harold
and Monti, Stefano
and Montano, Monty
and Steinberg, Martin H.
and Perls, Thomas T.
and Sebastiani, Paola},
title={Learning Bayesian Networks from Correlated Data},
year={2016},
month={May},
day={05},
publisher={The Author(s) SN  -},
volume={6},
pages={25156 EP  -},
note={Article},
url={http://dx.doi.org/10.1038/srep25156}
}

@Article{pmid21901116,
   Author="Lankinen, M.  and Schwab, U.  and Kolehmainen, M.  and Paananen, J.  and Poutanen, K.  and Mykkanen, H.  and Seppanen-Laakso, T.  and Gylling, H.  and Uusitupa, M.  and Ore?i?, M. ",
   Title="{{W}hole grain products, fish and bilberries alter glucose and lipid metabolism in a randomized, controlled trial: the {S}ysdimet study}",
   Journal="PLoS ONE",
   Year="2011",
   Volume="6",
   Number="8",
   Pages="e22646"
}

@ARTICLE{2014arXiv1411.2581R,
   author = {{Ranganath}, R. and {Tang}, L. and {Charlin}, L. and {Blei}, D.~M.
	},
    title = "{Deep Exponential Families}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1411.2581},
 primaryClass = "stat.ML",
 keywords = {Statistics - Machine Learning, Computer Science - Learning},
     year = 2014,
    month = nov,
   adsurl = {http://adsabs.harvard.edu/abs/2014arXiv1411.2581R},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{Piironen2017a,
author = "Piironen, Juho and Vehtari, Aki",
doi = "10.1214/17-EJS1337SI",
fjournal = "Electronic Journal of Statistics",
journal = "Electron. J. Statist.",
number = "2",
pages = "5018--5051",
publisher = "The Institute of Mathematical Statistics and the Bernoulli Society",
title = "Sparsity information and regularization in the horseshoe and other shrinkage priors",
url = "https://doi.org/10.1214/17-EJS1337SI",
volume = "11",
year = "2017"
}

@Article{Piironen2017b,
author="Piironen, Juho
and Vehtari, Aki",
title="Comparison of Bayesian predictive methods for model selection",
journal="Statistics and Computing",
year="2017",
month="May",
day="01",
volume="27",
number="3",
pages="711--735",
abstract="The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their differences and give recommendations about the preferred approaches. We focus on the variable subset selection for regression and classification and perform several numerical experiments using both simulated and real world data. The results show that the optimization of a utility estimate such as the cross-validation (CV) score is liable to finding overfitted models due to relatively high variance in the utility estimates when the data is scarce. This can also lead to substantial selection induced bias and optimism in the performance evaluation for the selected model. From a predictive viewpoint, best results are obtained by accounting for model uncertainty by forming the full encompassing model, such as the Bayesian model averaging solution over the candidate models. If the encompassing model is too complex, it can be robustly simplified by the projection method, in which the information of the full model is projected onto the submodels. This approach is substantially less prone to overfitting than selection based on CV-score. Overall, the projection method appears to outperform also the maximum a posteriori model and the selection of the most probable variables. The study also demonstrates that the model selection can greatly benefit from using cross-validation outside the searching process both for guiding the model size selection and assessing the predictive performance of the finally selected model.",
issn="1573-1375",
doi="10.1007/s11222-016-9649-y",
url="https://doi.org/10.1007/s11222-016-9649-y"
}

@WEB{uef,
nameweb = {University of Eastern Finland},
note =    {created on 10/22/2009},
url =     {http://www.uef.fi},
urldate = {2016-09-05}
}

@WEB{physmat,
nameweb = {Department of Physics and Mathematics},
url = 	  {http://www.uef.fi/en/web/fysmat},
urldate = {2016-09-05}
}

@WEB{tenk,
nameweb = {Finnish Advisory Board on Research Integrity},
url =     {http://www.tenk.fi/en/resposible-conduct-research-guidelines},
urldate = {2016-08-31}
}