000			03636aam a2200313 i 4500
999			_c77448 _d77448
003			CO-UCACDB
005			20230801214733.0
008			190515b2016 xxuad||fr|||| 001 0 eng d
020			_a9781118740651
040			_aCO-UCAC _beng _cCO-UCAC
041	0		_aeng
082	0	4	_222 _a519.23 _cD634 _d2016
100	1		_924404 _aDobrow, Robert P.
245	1	0	_aIntroduction to stochastic processes with R / _cRobert P. Dobrow
264		1	_aHoboken : _bJohn Wiley & Sons, _c2016.
300			_a479 páginas ; _bilistraciones, cuadros, gráficas ; _c16 x 24 cm.
505			_a1. Introduction and review. 2. Markov chains: first steps. 3. Markov chains for the long term. 4. Branching processes. 5. Markov chain Monte Carlo. 6. Poisson process. 7. Continuous-time Markov chains. 8. Brownian motion. 9. A gentle introduction to stochastics calculus. A. Getting started with. B. Probability review. C. Summary of common probability distributions. D. Matrix algebra review. Answers to selectec odd-numbered exercices.
505			_a1. Introducción y revisión. 2. Cadenas de Markov: primeros pasos. 3. Cadenas de Markov a largo plazo. 4. Procesos de bifurcación. 5. La cadena de Markov Monte Carlo. 6. Proceso de Poisson. 7. Cadenas de Markov de tiempo continuo. 8. Movimiento browniano. 9. Una suave introducción al cálculo estocástico. A. Empezando con. B. Revisión de probabilidad. C. Resumen de las distribuciones de probabilidad comunes. D. Matrix revisión de álgebra. Respuestas a los ejercicios de número impar selectec.
506			_aSistemas
520	3		_2Introduction to Stochastic Processes with R is an accessible and well-balanced presentation of the theory of stochastic processes, with an emphasis on real-world applications of probability theory in the natural and social sciences. The use of simulation, by means of the popular statistical software R, makes theoretical results come alive with practical, hands-on demonstrations.
520	3		_2Written by a highly-qualified expert in the field, the author presents numerous examples from a wide array of disciplines, which are used to illustrate concepts and highlight computational and theoretical results. Developing readers’ problem-solving skills and mathematical maturity, Introduction to Stochastic Processes with R features:
520	3		_2• More than 200 examples and 600 end-of-chapter exercises • A tutorial for getting started with R, and appendices that contain review material in probability and matrix algebra • Discussions of many timely and stimulating topics including Markov chain Monte Carlo, random walk on graphs, card shuffling, Black–Scholes options pricing, applications in biology and genetics, cryptography, martingales, and stochastic calculus • Introductions to mathematics as needed in order to suit readers at many mathematical levels • A companion web site that includes relevant data files as well as all R code and scripts used throughout the book.
520	3		_2Introduction to Stochastic Processes with R is an ideal textbook for an introductory course in stochastic processes. The book is aimed at undergraduate and beginning graduate-level students in the science, technology, engineering, and mathematics disciplines. The book is also an excellent reference for applied mathematicians and statisticians who are interested in a review of the topic.
650	0	7	_2Armarc _aPROCESOS ESTOCÁSTICOS _924405
650	0	7	_2Armarc _aPROBABILIDADES _9239787
650	0	7	_2Armarc _aR (LENGUAJE DE PROGRACIÓN DE COMPUTADORES) _924406
650	0	7	_2Armarc _aESTADÍSTICA MATEMÁTICA _9917
942			_2ddc _cBK