@book {IOPORT.01702341,
    author       = {Koski, Timo},
    title        = {Hidden Markov models for bioinformatics.},
    year         = {2001},
    isbn         = {1-4020-0135-5},
    pages        = {xvii, 391~p.},
    publisher    = {Dordrecht: Kluwer Academic Publishers},
    abstract     = {The purpose of this book is to give a thorough and systematic introduction to probabilistic modelling in bioinformatics, the special attention being paid to the kind of probabilistic models useful in genome analysis. The model families and the methods are presented in an order of increasing complexity and flexibility, in three steps, starting from the multinomial processes through Markov chains to hidden Markov models, including a number of sophisticated modifications (e.g., frame-dependent models, mixture transition models and hidden semi-Markov models) which are necessary in successful biological sequence analysis. Questions of parametric inference, selection between model families and various architectures are treated. This book is originally intended for advanced undergraduate and graduate students with a fairly limited background in probability theory, but otherwise well trained in mathematics and already familiar with some techniques of algorithmic sequence analysis.},
    reviewer     = {Ivan K\v{r}iv\'y (Ostrava)},
    identifier   = {01702341},
}