Collaborating Authors

Scientific reasoning on paper


Helping students develop skills in both critical thinking and scientific reasoning is fundamental to science education. However, the relationship between these two constructs remains largely unknown. Dowd et al. examined this issue by investigating how students' critical thinking skills related to scientific reasoning in the context of undergraduate thesis writing. The authors used the BioTAP rubric to assess scientific reasoning and the California Critical Thinking Skills Test to assess critical thinking. Results support the role of inference in scientific reasoning in writing, while also revealing other aspects of scientific reasoning (epistemological considerations and writing conventions) not related to critical thinking.

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Scalable Variational Inference for Dynamical Systems Machine Learning

Gradient matching is a promising tool for learning parameters and state dynamics of ordinary differential equations. It is a grid free inference approach which for fully observable systems is at times competitive with numerical integration. However for many real-world applications, only sparse observations are available or even unobserved variables are included in the model description. In these cases most gradient matching methods are difficult to apply or simply do not provide satisfactory results. That is why despite the high computational cost numerical integration is still the gold standard in many applications. Using an existing gradient matching approach, we propose a scalable variational inference framework, which can infer states and parameters simultaneously, offers computational speedups, improved accuracy and works well even under model misspecifications in a partially observable system.

Abduction, Reason and Science

AITopics Original Links

This volume explores abduction (inference to explanatory hypotheses), an important but neglected topic in scientific reasoning. My aim is to inte grate philosophical, cognitive, and computational issues, while also discuss ing some cases of reasoning in science and medicine. The main thesis is that abduction is a significant kind of scientific reasoning, helpful in delineating the first principles of a new theory of science. The status of abduction is very controversial. When dealing with abduc tive reasoning misinterpretations and equivocations are common.

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems

Neural Information Processing Systems

Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. In this paper, we present a nonparametric approach to the learning of an unknown number of persistent, smooth dynamical modes by utilizing a hierarchical Dirichlet process prior. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with an efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, and the IBOVESPA stock index.