Comments on: model vs model http://hea-www.harvard.edu/AstroStat/slog/2007/model-vs-model/ Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders Fri, 01 Jun 2012 18:47:52 +0000 hourly 1 http://wordpress.org/?v=3.4 By: hlee http://hea-www.harvard.edu/AstroStat/slog/2007/model-vs-model/comment-page-1/#comment-112 hlee Tue, 09 Oct 2007 22:01:49 +0000 http://hea-www.harvard.edu/AstroStat/slog/2007/model-vs-model/#comment-112 <p>I recommend a book by Thomas and Cover, <a href="http://www.amazon.com/Elements-Information-Theory-Telecommunications-Processing/dp/0471241954/ref=pd_bbs_sr_1/105-7811049-9445239?ie=UTF8&s=books&qid=1191966889&sr=8-1 " rel="nofollow">Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)</a> and a paper by Shannon, <a href="http://plan9.bell-labs.com/cm/ms/what/shannonday/paper.html" rel="nofollow">A mathematical theory of communication.</a> Particularly, the book is very good for general purposes (coding, data compression, signal/image processing, and filter design; I heard many CS/EE departments use this book in their required course works) and it contains quite many statistical theorems, which are the bases of developing a data model, or an object-oriented approach to write a code.</p> <p><strong>[Added]</strong> <a href="http://www.elementsofinformationtheory.com/" rel="nofollow">Elements of Information Theory</a> has its own website: http://www.elementsofinformationtheory.com/ Some years ago, I was able to find many course websites that said <i>required textbook</i> and contained problems, solutions, and relevant research topics including <a href="http://www.stanford.edu/class/ee376b/" rel="nofollow">one of the authors' course website at Stanford</a>. A personal wish is that statistics departments offer Information Theory related courses with a cross opening to astronomy students.</p> I recommend a book by Thomas and Cover, Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) and a paper by Shannon, A mathematical theory of communication. Particularly, the book is very good for general purposes (coding, data compression, signal/image processing, and filter design; I heard many CS/EE departments use this book in their required course works) and it contains quite many statistical theorems, which are the bases of developing a data model, or an object-oriented approach to write a code.

[Added] Elements of Information Theory has its own website: http://www.elementsofinformationtheory.com/ Some years ago, I was able to find many course websites that said required textbook and contained problems, solutions, and relevant research topics including one of the authors’ course website at Stanford. A personal wish is that statistics departments offer Information Theory related courses with a cross opening to astronomy students.

]]> By: hlee http://hea-www.harvard.edu/AstroStat/slog/2007/model-vs-model/comment-page-1/#comment-111 hlee Fri, 05 Oct 2007 21:28:50 +0000 http://hea-www.harvard.edu/AstroStat/slog/2007/model-vs-model/#comment-111 Due to the binary nature of data model and computer science, interpreting data model into statistical one for the inference purpose comes smoothly, in contrast to astronomers' model. The challenges lie in developing computer scientific theories, most of which can be associated with already existing theories from mathematical statistics. However, this is not always true when it comes to Information Theory. <blockquote><b>[Response:</b> Hmm. I don't see how the data model can have relevance to statistical inference. It is not binary. It is essentially imposing an object-oriented approach which may help in the writing of generalized Bayesian code easier, but other than that, it doesn't have any connection to statistics. It might help to make your programs run better (maybe even faster) and be written in a more scalable fashion. -vlk<b>]</b></blockquote> Due to the binary nature of data model and computer science, interpreting data model into statistical one for the inference purpose comes smoothly, in contrast to astronomers’ model. The challenges lie in developing computer scientific theories, most of which can be associated with already existing theories from mathematical statistics. However, this is not always true when it comes to Information Theory.

[Response: Hmm. I don't see how the data model can have relevance to statistical inference. It is not binary. It is essentially imposing an object-oriented approach which may help in the writing of generalized Bayesian code easier, but other than that, it doesn't have any connection to statistics. It might help to make your programs run better (maybe even faster) and be written in a more scalable fashion.
-vlk]

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