Edward – Linear Mixed Effects Models
Linear Mixed Effects Models With linear mixed effects models, we wish to model a linear relationship for data points with inputs of varying type, categorized into subgroups, and associated to a real-valued output. We demonstrate with an example in Edward. An interactive version with Jupyter notebook is available here. Data We use the InstEval data set from the popular lme4 R package (Bates, Mächle
Edward – Tutorials
Tutorials Edward provides a testbed for rapid experimentation and research with probabilistic models. Here we show how to apply this process for diverse learning tasks. Bayesian linear regression A fundamental model for supervised learning. Batch training How to train a model using only minibatches of data at a time. TensorBoard Visualize learning, explore the computational graph, and diagnose tra
Edward – Home
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and mach
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Edward – ed.ImplicitKLqp
