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Product details

File Size: 3084 KB

Print Length: 290 pages

Page Numbers Source ISBN: 1447163079

Publisher: Springer; 2 edition (January 14, 2014)

Publication Date: January 14, 2014

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B00HV5YYUY

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Amazon Best Sellers Rank:

#1,521,018 Paid in Kindle Store (See Top 100 Paid in Kindle Store)

I am an engineer and I read the book. It was a fairly quick read and I learned a lot. I used some of the algorithms for work. I recommend the book. Though I had some knowledge of HMM, this could be a good introduction.

Even if you don't speak German.Kidding aside, the language in this book is convoluted, obtuse and obviously German lightly translated. I am still working on the introductory statistics section, but I find it easier to read about the concepts mentioned elsewhere online, like on wikipedia. The issues are confused, unnecessarily general, pedantic and overly mathematical. I am a very good programmer but not super mathematical so a more algorithmic approach to the subject would have been better for me. I'll update my review when I get the applied part of the book, but the applied sections don't seem to let up on the mathematical notation. Pseudo code or C or any kind of algorithmic description would have been appreciated.

Excellent book, can't say enough good things about it. The coverage is well thought out and the explanations are extremely thorough. Sure, there are some downsides: it's definitely written in 'German English' (as others have mentioned), and the Bioinformatics references seem to gloss over dropouts (out of scope for the book, but should be mentioned all the same). The problems are minor, and it's a great reference/reason for expanding your research into other areas if breadth and depth are your goals.

Though, at times, it shows that the author is German (I mean, in the construction of statements), and the use of commas is a bit slack, I'm very impressed by the clarity (and also, by the practicality) of the exposition. I have not finished the book yet, but nearing the first half of it, almost every page teaches me, of clarifies me, some useful concept. I've tried my hand at Hidden Markov Models with some classical papers (e.g. Rabiner) and books (e.g. MacDonald and Zucchini) ... I wish I had started here.

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