This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established conditions under which bubbles present within class of mixed causal–noncausal vector autoregressive models, we suggest statistical tools detect locally explosive dynamics a Student t-distribution maximum likelihood framework. The perfor...

Abstract We propose quantitative probing as a model-agnostic framework for validating causal models in the presence of domain knowledge. The method is constructed analogy to train/test split correlation-based machine learning. It consistent with logic scientific discovery and enhances current validation strategies. effectiveness illustrated using Pearl’s sprinkler example, before thorough simul...

In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of models, relational can represent and reason about complex dynamic systems exhibiting such feedback loops. Existing cyclic discovery algorithms for learning models from observational data assume...

The development of high-resolution imaging methods such as electron and scanning probe microscopy atomic tomography have provided a wealth information on structure functionalities solids. availability this data in turn necessitates approaches to derive quantitative physical information, much like the scattering early XX century which given one most powerful tools condensed matter physics arsena...