We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating the parameters of a fixed graph. We argue that a complete account of causal induction should also consider how people learn the underlying causal graph structure, and we propose to model this inductive process as a Bayes...

We present a process algebra suitable to the modelling of timed concurrent systems and to their efficient verification through model checking. The algebra is provided with two consistent semantics: a structural operational semantics (as usual for process algebras) and a denotational semantics in terms of Petri nets in which time is introduced through counters of explicit clock ticks. This way o...

Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause-effect fashion. How does such immediate causation, tha...

Models offer visual support for analyzing complex domains such as business processes and information systems. In both cases, models are developed using graphical modelling languages. In our study we focus on usability evaluation of modelling languages for the model interpretation scenario. The study is based on a causal model of hypotheses, which was developed under consideration of psychologic...

It was recently realised that quantum theory allows for so-called causally nonseparable processes, which are incompatible with any definite causal order. This was first suggested on a rather abstract level by the formalism of process matrices, an extension of the quantum formalism which only assumes that quantum theory holds locally in some observers' laboratories, but does not impose a global ...

Business processes have been exploiting the continual growth of information technology, and as a result, these are becoming very complex with time. Without adequate IT support, a business organization can hardly survive these days. For an organization, IT acquisition involves two important process dimensions, one, suppliers’ capability to adequately evaluate the IT needs of the organization and...

The paper considers local linear regression of a time series model with non-stationary regressors and errors. Asymptotic property of the local linear estimator is derived under a new dependence measure of non-stationary time series. We apply the local linear regression method to estimate the ‘‘time-varying’’ coefficients of an economic-causal model for the industrial sector of the U.S. economy....

,4bsrract: Adaptive control schemes usually depend on estimation of system parameters, which in turn depends excitation of modes associated with these parameters. Such excitation is supplied by plant noise, the adaptive control signal itself, and any external excitation such as a reference signal. In this note, it is showm that there is a universality advantage for any externally applied signal...

Different directed acyclic graphs (DAGs) may be Markov equivalent in the sense that they entail the same conditional independence relations among the observed variables. Meek (1995) characterizes Markov equivalence classes for DAGs (with no latent variables) by presenting a set of orientation rules that can correctly identify all arrow orientations shared by all DAGs in a Markov equivalence cla...

In this paper, we describe a generic approach to modelling dynamics in neuronal populations. This approach models a full density on the states of neuronal populations but finesses this high-dimensional problem by re-formulating density dynamics in terms of ordinary differential equations on the sufficient statistics of the densities considered (c.f., the method of moments). The particular form ...