Applied stochastic processes: a biostatistical and population oriented approach
This book is an extension of the author's former work Stochastic Processes in Demography and Applications. This extension expands the scope of the earlier book to focus on and encompass the various techniques of applied stochastic processes with orientation or emphasis on biostatistics including statistical genetics and survival analysis.
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RANDOM WALK AND MARKOV PROCESS
TECHNIQUES OF STOCHASTIC PROCESS
NONMARKOV PROCESS AND RENEWAL THEORY
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Applications assume asymptotic Biometric Journal birth and death Biswas bivariate censored central limit theorem conditional probability consider Counter Model dead death process defined denote density function deterministic differential equation dN(x duration epidemic model Example exponential failure female fertility follows gametes gene genotypes given hazard rate Hence independent individual integer interval John Wiley Kolmogorov's Laplace transform Markov chain Markov process Martingale Martingale w.r. mortality number of events number of infected o(At obtain Palm probability parameter Poisson process probability distribution probability generating function probability of dying problem Proof Putting random variable random walk renewal represents respectively result sequence Similarly solution Statistics stochastic model Stochastic Processes stopping rule success Super Martingale survival function surviving theory treatment upto vector vide waiting time distribution Wiley & Sons zero