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@Article{Bauer2019,
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@Article{BenAkiva1993,
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@Article{Blackwell1973,
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@Article{Bliss1934,
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@Article{Gelman1992,
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@Article{Geweke1998,
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@Article{Gronau2017,
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@InBook{Hammersley1964,
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  chapter   = {5},
  editor    = {Bartlett, M. S. and Cox, D. R.},
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  publisher = {Springer Netherlands},
  title     = {General principles of the {Monte Carlo} method},
  year      = {1964},
  address   = {Dordrecht},
  isbn      = {978-94-009-5819-7},
  abstract  = {Every Monte Carlo computation that leads to quantitative results may be regarded as estimating the value of a multiple integral. For suppose that no computation requires more than N(= 1010 say) random numbers; then the results will be a (vector-valued) function 5.1.1{\$}{\$}R{\backslash}left( {\{}{\{}{\backslash}xi {\_}1{\}},{\backslash}xi , {\backslash}ldots ,{\{}{\backslash}xi {\_}N{\}}{\}} {\backslash}right){\$}{\$}of the sequence of random numbers $\xi$1,$\xi$2,{\ldots}. This is an unbiased estimator of 5.1.2{\$}{\$}{\backslash}int{\_}0^1 {\{}...{\}} {\backslash}int{\_}0^1 {\{}{\}} R{\backslash}left( {\{}{\{}x{\_}1{\}}....,{\{}x{\_}N{\}}{\}} {\backslash}right)d{\{}x{\_}1{\}}...d{\{}x{\_}N{\}}.{\$}{\$}},
  booktitle = {Monte Carlo Methods},
  doi       = {10.1007/978-94-009-5819-7_5},
  groups    = {RprobitB package},
  url       = {https://doi.org/10.1007/978-94-009-5819-7_5},
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@Article{Hewig2011,
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@Article{Imai2005,
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@Book{Jeffreys1998,
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@Article{Li2019,
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@Book{Marin2014,
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  publisher = {Springer Verlag},
  title     = {{Bayesian Essentials with R}},
  year      = {2014},
  doi       = {10.1007/978-1-4614-8687-9},
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@Article{McCulloch1994,
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  doi     = {10.1016/0304-4076(94)90064-7},
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@Book{McElreath2020,
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  publisher = {Chapman and Hall/CRC},
  title     = {Statistical rethinking: a {Bayesian} course with examples in {R} and {Stan}},
  year      = {2020},
  doi       = {10.1201/9780429029608},
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@Article{Meijer2006,
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  journal   = {Journal of Applied Econometrics},
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  year      = {2006},
  number    = {2},
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  volume    = {21},
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  publisher = {Wiley Online Library},
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@Article{Neal2000,
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  title   = {Markov chain sampling methods for {Dirichlet} process mixture models},
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  number  = {2},
  volume  = {9},
  doi     = {10.2307/1390653},
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@Article{Newton1994,
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  publisher = {Wiley Online Library},
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@Article{Nobile1998,
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  volume  = {8},
  doi     = {10.1023/A:1008905311214},
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@Article{Rasmussen1999,
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  journal = {Advances in neural information processing systems},
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@Article{Sachs2017,
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  number  = {2},
  pages   = {1--19},
  volume  = {79},
  doi     = {10.18637/jss.v079.c02},
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@Article{Schwarz1978,
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  journal = {The Annals of Statistics},
  title   = {Estimating the dimension of a model},
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@Article{Sethuraman1994,
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  journal   = {Statistica Sinica},
  title     = {A constructive definition of dirichlet priors},
  year      = {1994},
  number    = {2},
  pages     = {639--650},
  volume    = {4},
  groups    = {RprobitB package},
  publisher = {Institute of Statistical Science, Academia Sinica},
  url       = {http://www.jstor.org/stable/24305538},
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@Book{Train2009,
  author    = {Train, K.},
  publisher = {Cambridge University Press},
  title     = {Discrete choice methods with simulation},
  year      = {2009},
  doi       = {10.1017/CBO9780511805271},
  groups    = {RprobitB package},
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@Article{Tversky1988,
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  journal = {Decision making: Descriptive, normative, and prescriptive interactions},
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  year    = {1988},
  pages   = {167--192},
  groups  = {RprobitB package},
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@Article{Watanabe2010,
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  journal = {Journal of Machine Learning Research},
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  year    = {2010},
  number  = {12},
  volume  = {11},
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