000			02127cam a22003614a 4500
001			16528275
003			IIITD
005			20140707193419.0
008			101102s2011 flua b 001 0 eng
010			_a 2010043676
015			_aGBB060839 _2bnb
016	7		_a015551232 _2Uk
020			_a9781439818824
035			_a(OCoLC)ocn615883171
040			_aDLC _cDLC _dYDX _dUKM _dYDXCP _dBTCTA _dCDX _dPUL _dINU _dDLC
042			_apcc
050	0	0	_aQA274 _b.A63 2011
082	0	0	_a519.23 _222 _bALL-I
084			_aMAT003000 _aMAT029010 _2bisacsh
100	1		_aAllen, Linda J. S.
245	1	3	_aAn introduction to stochastic processes with applications to biology _cLinda J. S. Allen.
250			_a2nd ed.
260			_aBoca Raton, FL : _bChapman & Hall/CRC, _c©2011.
300			_axxiv, 466 p. : _bill. ; _c25 cm.
505	0	0	_tMean First Passage Time -- _tAn Example: Genetics Inbreeding Problem -- _tUnrestricted Random Walk in Higher Dimensions -- _g2.10.1. _tTwo Dimensions --
505	0	0	_g3.7. _tLogistic Growth Process -- _g3.8. _tQuasistationary Probability Distribution -- _g3.9. _tSIS Epidemic Model -- _g3.9.1. _tDeterministic Model -- _g3.9.2. _tStochastic Model -- _g3.10. _tChain Binomial Epidemic Models --
520			_a"The second edition of a bestseller, this textbook delineates stochastic processes, emphasizing applications in biology. It includes MATLAB throughout the book to help with the solutions of various problems. The book is organized according to the three types of stochastic processes: discrete time Markov chains, continuous time Markov chains and continuous time and state Markov processes. It contains a new chapter on the biological applications of stochastic differential equations and new sections on alternative methods for derivation of a stochastic differential equation, data and parameter estimation, Monte Carlo simulation, and more"--
650		0	_aStochastic processes.
650		0	_aBiomathematics.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c10137 _d10137