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Binder 2.0 - Reproducible, interactive, sharable environments for science at scale
TLDR
Several of the design decisions and goals that went into the development of the current generation of Binder are detailed.
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Ockham’s Razor Cuts to the Root: Simplicity in Causal Explanation
TLDR
It is suggested that a preference for root-simpler explanations follows from the role of explanations in highlighting and efficiently representing and communicating information that supports future predictions and interventions.
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nbgrader: A Tool for Creating and Grading Assignments in the Jupyter Notebook
nbgrader is a flexible tool for creating and grading assignments in the Jupyter Notebook (Kluyver et al., 2016). nbgrader allows instructors to create a single, master copy of an assignment,…
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Evaluating computational models of explanation using human judgments
TLDR
Across two versions of two causal structures and across both experiments, it is found that the Causal Explanation Tree and Most Relevant Explanation models provide better fits to human data than either Most Probable Explanation or Explanation Trees.
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A rational model of causal induction with continuous causes
TLDR
This model successfully predicts human judgments from previous studies better than models of discrete causal inference, and outperforms several other plausible models of causal induction with continuous causes in accounting for people's inferences in a new experiment.
Elements of a rational framework for continuous-time causal induction
TLDR
A rational framework for causal induc- tion from events that take place in continuous time is presented and a set of desiderata for a computational frame- work for human continuous-time causal inference is outlined, based on rational statistical inference over continuous- time stochastic processes.
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Upsetting the contingency table: Causal induction over sequences of point events
TLDR
This work presents a formal framework for modeling causal inferences about sequences of point events, based on Bayesian inference over nonhomogeneous Poisson processes (NHPPs) and shows how to apply this framework to successfully model data from an experiment by Lagnado and Speekenbrink (2010) which examined human learning from sequences of points.
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A rational model of causal inference with continuous causes
TLDR
This model successfully predicts human judgments from previous studies better than models of discrete causal inference, and outperforms several other plausible models of causal induction with continuous causes in accounting for people's inferences in a new experiment.
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Reproducing Machine Learning Research on Binder
The adoption of open-source tools to write machine learning pipelines, often in Python [66], has 9 provided researchers with access to an author’s experiments or allowed them to replicate a study 10…
Caching Algorithms and Rational Models of Memory
TLDR
Anderson and Schooler’s model performed comparably to the worst caching algorithms, but was the only model that captured the spacing effects seen in human memoryData, which showed similar effects of recency and practice.
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