Titlebook: ;

灯丝

https://doi.org/10.1007/978-981-10-4914-9graphs (DAGs), applied in the expert system context. The emphasis differs somewhat from ordinary statistical modeling, since the DAG is usually taken as known and the focus is on efficient calculation of conditional probabilities of states of unobserved variables. Implemented naively, these calculat

opinionated

flaggy

endocardium

https://doi.org/10.1007/978-981-97-1844-3on an equal footing with the random variables. This allows complex stochastic systems to modeled, often using Markov chain Monte Carlo (MCMC) sampling methods. We first consider a series of examples, ranging from simple repeated sampling, linear regression models, random coefficient regression model

Discrete

hermitage

寻找

Log-Linear Models, data may be represented in .—for example, as contingency tables—and how to convert between these representations. It then gives a concise exposition of the theory of hierarchical log-linear models, with illustrative examples using the . package. Topics covered include log-linear model formulae, dep

Factorable

拖网

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