Stochastic Modeling of Scientific Data

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Combining stochastic modeling with statistical inference, this text covers a range of models, including point processes and hidden Markov models. It uniquely integrates probability theory with statistical model fitting, featuring various datasets for analysis and exercises. The book introduces Markov chain Monte Carlo methods for complex model likelihood evaluations and presents the forward-backward algorithm for hidden Markov models. With examples from diverse fields like astronomy and genetics, it serves as an excellent resource for researchers and graduate students in applied probability and stochastic processes.