from IPython.display import Image;
Image('AsteriskData.png', width=200)
A quick hello world example for beginners to bnpy.
Image('GaussianToyData-LearnedClusters.png', width=200)
Experiment showing how bnpy makes it easy to compare different initialization routines across many algorithm runs.
Image('GaussianToyData-CompareInitMethods.png', width=200)
Experiment showing how births and merges can find the ideal set of clusters, no matter how many we have initially.
Image('GaussianToyData-BirthMergeKvsTime.png', width=200)
Image('BarsData.png', width=200)
Experiment showing how births and merges add and remove clusters to find the ideal set of bars.
Image('BarsData-BirthMergeKvsTime.png', width=200)
Experiment shows the basics for training a topic model, comparing different number of topics.
Image('BarsToyData-LearnedTopicsFixedK=10.png', width=200)
Use merge and delete moves for topic models to identify the 10 true bars topics from initializations with many more.
Image('BarsToyData-LearnedTopics-MergeDelete.png', width=200)