Principled Statistical Inference In Data Science
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COMPUTER AGE STATISTICAL I NF ER C
hastie.su.domainsunderstand each method™s roles in inference and/or prediction.fl Š Galit Shmueli, National Tsing Hua University fiA masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-˜rst century.fl
Basic Principles of Statistical Inference
imai.fas.harvard.eduData and computing revolutions in the 21st century The world is stochastic rather than deterministic Probability theory used to model stochastic events Statistical inference: Learning about what we do not observe (parameters) using what we observe (data) Without statistics:wildguess With statistics: principled guess 1 assumptions 2 formal ...
Introduction to Statistical Machine Learning
kioloa08.mlss.ccof statistical machine learning, which is concerned with the development of algorithms and techniques that learn from observed data by constructing stochastic models that can be used for making predictions and decisions. Topics covered include Bayesian inference and maximum likelihood modeling; regression, classiflcation, density estimation,
Indoor Segmentation and Support Inference from RGBD …
cs.nyu.eduIndoor Segmentation and Support Inference from RGBD Images Nathan Silberman 1, Derek Hoiem2, Pushmeet Kohli3, Rob Fergus 1Courant Institute, New York University 2Department of Computer Science, University of Illinois at Urbana-Champaign 3Microsoft Research, Cambridge Abstract. We present an approach to interpret the major surfaces, ob-
Introduction to Bayesian Learning
www.dgp.toronto.eduLearning techniques promise the best of both worlds: starting from some captured data, we can proce-durally synthesize more data in the style of the original. Moreover, we can constrain the synthetic data, for example, according to the requirements of an artist. Of course, we must begin with some data produced by an artist or a capture session.
Maximum Entropy Inverse Reinforcement Learning
www.aaai.orgcounts, fζ = P sj∈ζ fsj, which are the sum of the state fea- tures along the path. reward(fζ) = θ⊤fζ =X sj∈ζ θ⊤f sj The agent demonstrates single trajectories, ζ˜ i, and has an expected empirical feature count, ˜f = 1 m P i fζ˜ i, based on many (m) demonstrated trajectories.