Based on a lecture course given at Chalmers University of Technology, this 2002 book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before applying it to study a range of randomized algorithms with important applications in optimization and other problems in computing. Amongst the algorithms covered are the Markov chain Monte Carlo method, simulated annealing, and the recent Propp-Wilson algorithm. This book will appeal not only to mathematicians, but also to students of statistics and computer science. The subject matter is introduced in a clear and concise fashion and the numerous exercises included will help students to deepen their understanding.
Of einföld og stutt bók til að kenna í alvöru áfanga. Of mörgum smáatriðum sleppt svo það er erfitt að fá almennilegan skilning. Samt góð bók ef maður hefur ekki mikinn tíma og vil læra sem mest á þessum stutta tíma.
What a great book. Concise and formal where it must be, yet with lots of examples, footnotes and prose strengthening the intuitive understanding and giving the big picture (also, there were jokes in this book. Some are subtle. At least I think they are jokes.) I loved that the exercises have a number indicating their expected time needed to solve next to them. Some solutions would be nice, though. I greatly enjoyed the choice of applications. The treatment of MCMC was fantastic. 10/10, would read again.