This paper includes a causal-based modelling of software process models in order to analyse the correct relationships between the different (key) process areas of these models. A first short description of causal network approaches shows the identified problems and possible benefits using these formal techniques in the software engineering area. The definition and extension of the causal modell...

Extraction of Causal-Association Networks from Unstructured Text Data

A new approach to quantum gravity is described which joins the loop representation formulation of the canonical theory to the causal set formulation of the path integral. The theory assigns quantum amplitudes to special classes of causal sets, which consist of spin networks representing quantum states of the gravitational field joined together by labeled null edges. The theory exists in 3+1, 2+...

Bayes nets are seeing increasing use in expert systems [2, 6], and structural equations models continue to be popular in many branches of the social sciences [1]. Both types of models involve directed acyclic graphs with variables as nodes, and in both cases there is much mysterious talk about causal interpretation. This paper uses probability trees to give precise conditions under which Bayes ...

Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based...

Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks-[X→Y→Z, X←Y→Z, X→Y←Z]-supply answers for questions like, "Suppose both X and Y occur, what is the probability Z occurs?" or "Suppose you intervene and make Y occur, w...

The generation of pleasant prosody parameters is very important for speech synthesis. A prosody generation unit can be seen as a dynamical system. In this paper sophisticated time-delay recurrent neural network (NN) topologies are presented which can be used for the modeling of dynamical systems. Within the prosody prediction task left and right context information is known to influence the pre...

Knowing the cause and effect is important to researchers who are interested in modeling the effects of actions, and Artificial Intelligence researchers are among them. One commonly used method for modeling cause and effect is graphical model. Bayesian Network is a probabilistic graphical model for representing and reasoning uncertain knowledge. It has been used as a fundamental tool and is beco...

The generation of pleasant prosody parameters is very important for speech synthesis. A Prosody generation unit can be seen as a dynamical system. In this paper sophisticated time-delay recurrent neural network (NN) topologies are presented which can be used for the modeling of dynamical systems. Within the prosody prediction task left and right context information is known to influence the pre...

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describi...