Ditemukan 10 dokumen yang sesuai dengan query
New York: Academic Press, 1982
519.54 STA
Buku Teks Universitas Indonesia Library
Pratt, John W., author
Cambridge, UK: Massachusetts Institute of Technology, 1995
519.542 PRA i
Buku Teks Universitas Indonesia Library
Orlvoski, Sergei A., author
New York: Allerton Press, 1994
511.32 ORL c
Buku Teks Universitas Indonesia Library
Lindley, D.V., author
A study of those statistical ideas that use a probability distribution over parameter space. The first part describes the axiomatic basis in the concept of coherence and the implications of this for sampling theory statistics. The second part discusses the use of Bayesian ideas in many branches of statistics...
Philadelphia: Society for Industrial and Applied Mathematics, 1995
e20451236
eBooks Universitas Indonesia Library
Koller, Daphne, author
Cambridge, UK: MIT Press, 2009
519.542 KOL p
Buku Teks Universitas Indonesia Library
Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent...
Berlin: [Springer, ], 2012
e20398180
eBooks Universitas Indonesia Library
Gupta, Shanti S., author
Multiple Decision Procedures: Theory and Methodology of Selecting and Ranking Populations provides an encyclopedic coverage of the literature in the area of ranking and selection procedures, summarizing and surveying in a unified manner a majority of more than 600 main references in the bibliography. It also deals with related problems,...
Philadelphia: Society for Industrial and Applied Mathematics, 2002
e20450615
eBooks Universitas Indonesia Library
Lee, Herbert K.H., author
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This...
Philadelphia: Society for Industrial and Applied Mathematics, 2004
e20448023
eBooks Universitas Indonesia Library
Harnett, Donald L., author
New York: John Wiley & Sons, 1998
658.403 3 HAR d
Buku Teks Universitas Indonesia Library
Irvina Kamalitha Zunaidi, author
ABSTRAK
Data bawah permukaan merupakan data yang sangat dibutuhkan dalam menentukan besar cadangan hidrokarbon di Indonesia. Selain itu, kualitas dari cadangan itu sendiri menentukan perkembangan industri migas kedepannya. Pemerintah telah secara agresif mendorong penggunaan gas alam dan saat ini pemerintah belum memiliki data mengenai cadangan gas secara efisien. Oleh karena itu,...
2019
T54521
UI - Tesis (Membership) Universitas Indonesia Library
