The course shows the use of mathematical modeling in biological sciences. A variety of modeling techniques are Adler, Modeling the Dynamics of Life,. Computer networks are inherently social networks, linking people, organizations of computer networks has facilitated a deemphasis on group solidarities at. Bayesian Hierarchical Modelling using WinBUGS. Nicky Best, Alexina Mason and Philip Li. Sho...

Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between f...

Wiener diffusion accounts of human decision-making are among the most successful and best developed formal models in the psychological sciences. We reconsider these models from a Bayesian perspective, using graphical modeling, and Markov Chain Monte-Carlo methods for posterior sampling. By analyzing seminal data from a brightness discrimination task, we show how the Bayesian approach offers sev...

Although n-gram models are still the de facto standard in language modeling for speech recognition, more sophisticated models achieve better accuracy by taking additional information, such as syntactic rules, semantic relations or domain knowledge into account. Unfortunately, most of the effort in developing such models goes into the implementation of handcrafted inference routines. A generic m...

Bayesian networks are commonly used in cognitive student modeling and assessment. They typically represent the item-concepts relationships, where items are observable responses to questions or exercises and concepts represent latent traits and skills. Bayesian networks can also represent concepts-concepts and concepts-misconceptions relationships. We explore their use for modeling item-item rel...

In this chapter, we will discuss Bayesian networks, a currently widely accepted modeling class for reasoning with uncertainty. We will take a practical point of view, putting emphasis on modeling and practical applications rather than on mathematical formalities and the advanced algorithms that are used for computation. In general, Bayesian network modeling can be data driven. In this chapter, ...

A longitudinal study refers to collection of a response variable and possibly some explanatory variables at multiple follow-up times. In many clinical studies with longitudinal measurements, the response variable, for each patient is collected as long as an event of interest, which considered as clinical end point, occurs. Joint modeling of continuous longitudinal measurements and survival time...