Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. New continuous time Bayesian network classifiers are introduced while their conditional log-likelihood scoring function is developed. A learning algorithm, combining conditional log-likelihood with Bayesian parameter estimation is developed. Classification ac...

Likelihood methods are often difficult to use with large, irregularly sited spatial data sets, owing to the computational burden. Even for Gaussian models, exact calculations of the likelihood for n observations require O.n3/ operations. Since any joint density can be written as a product of conditional densities based on some ordering of the observations, one way to lessen the computations is ...

There are three assumptions of independence or conditional independence that underlie linkage likelihood computations on sets of related individuals. The first is the independence of meioses, which gives rise to the conditional independence of haplotypes carried by offspring, given those of their parents. The second derives from the assumption of absence of genetic interference, which gives ris...

Suppose we observe a geometrically ergodic semi-Markov process and have a parametric model for the transition distribution of the embedded Markov chain, for the conditional distribution of the inter-arrival times, or for both. The first two models for the process are semiparametric, and the parameters can be estimated by conditional maximum likelihood estimators. The third model for the process...

For an interest parameterψ(θ) the Bayesian method eliminates the nuisance parameterλ(θ) by integrating with respect to a conditional prior for λ given ψ. This conditional prior may be difficult to specify in both the subjective and objective Bayesian contexts. We propose the use of results from likelihood theory that give highly accurate third order determinations of various marginal distributi...