The existence of multiple likelihood maxima necessitates algorithms that explore a large part of the tree space. However, because of computational constraints, stepwise addition-based tree-searching methods do not allow for this exploration in reasonable time. Here, I present an algorithm that increases the speed at which the likelihood landscape can be explored. The iterative algorithm combine...

This approach has several advantages: (1) there is no need to estimate the variance/covariance at all, which may become prohibitively complicated for other procedures that requires the estimation of such. (2) When inverting the tests to obtain confidence regions/intervals, this procedure inherits all the good properties of a likelihood ratio test. (3) Free software implementation of the test is...

A new approach of parameterization is proposed to construct a general goodnessof-fit test. It can not only generate traditional tests (including the Kolmogorov–Smirnov, Cramér– von Mises and Anderson–Darling tests) but also produce new types of omnibus tests, which are generally much more powerful than the old ones.

In logistic regression analysis of small or sparse data sets, results obtained by classical maximum likelihood methods cannot be generally trusted. In such analyses it may even happen that the likelihood meets the convergence criteria while at least one parameter estimate diverges to +/-infinity. This situation has been termed 'separation', and it typically occurs whenever no events are observe...

The method of maximum likelihood (ML), introduced by Fisher (1921), is widely used in human and quantitative genetics and we draw upon this approach throughout the book, especially in Chapters 13–16 (mixture distributions) and 26–27 (variance component estimation). Weir (1996) gives a useful introduction with genetic applications, while Kendall and Stuart (1979) and Edwards (1992) provide more ...

A lot of work has been done in deriving the pronunciation dictionary automatically from training data. These attempts focussed mainly on maximum likelihood or similar techniques. Due to the complexity and variability of the pronunciation process it is di cult to nd an adequate pronunciation model. The model will deviate from the truth. Hence, the application of maximum likelihood techniques is ...

This study reports on the application of the knowledge discovery in database process to generate models that can predict the likelihood of falls among the elderly who reside in long-term care facilities. This process was applied to data held in the Minimum Data Set, a comprehensive resident assessment instrument being used in all Medicare and Medicaid supported nursing homes in the United State...

Many machine learning applications require a combination of probability and first-order logic. Markov logic networks (MLNs) accomplish this by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can be learned by maximizing the likelihood of a relational database, but this can be quite costly and lead ...

Prefactual thoughts typically take the form of implied or explicit if-then statements that represent mental simulations of alternatives to what is expected to occur in the future. The authors propose that the multiplicative combination of "if likelihood" (the degree to which the antecedent condition of the prefactual is perceived to be likely) and "then likelihood" (the perceived conditional li...

The present research explored the belief that it is bad luck to "tempt fate." Studies 1 and 2 demonstrated that people do indeed have the intuition that actions that tempt fate increase the likelihood of negative outcomes. Studies 3-6 examined our claim that the intuition is due, in large part, to the combination of the automatic tendencies to attend to negative prospects and to use accessibili...