Markov Chains In Python

Sandipan Dey shows off various uses of Markov chains as well as how to create one in Python:

Perspective. In the 1948 landmark paper A Mathematical Theory of Communication, Claude Shannon founded the field of information theory and revolutionized the telecommunications industry, laying the groundwork for today’s Information Age. In this paper, Shannon proposed using a Markov chain to create a statistical model of the sequences of letters in a piece of English text. Markov chains are now widely used in speech recognition, handwriting recognition, information retrieval, data compression, and spam filtering. They also have many scientific computing applications including the genemark algorithm for gene prediction, the Metropolis algorithm for measuring thermodynamical properties, and Google’s PageRank algorithm for Web search. For this assignment, we consider a whimsical variant: generating stylized pseudo-random text.

Markov chains are a venerable statistical technique and formed the basis of a lot of text processing (especially text generation) due to the algorithm’s relatively low computational requirements.

Related Posts

A Quick Keras Example

Shubham Dangare takes us through a quick example using Keras and TensorFlow in Python: Keras is a high-level neural networks API, written in Python and capable of running on top of Tensorflow, CNTK  or Theano. It was developed with a focus on enabling fast experimentation. In this blog, we are going to cover one small […]

Read More

Biases in Tree-Based Models

Nina Zumel looks at tree-based ensembling models like random forest and gradient boost and shows that they can be biased: In our previous article , we showed that generalized linear models are unbiased, or calibrated: they preserve the conditional expectations and rollups of the training data. A calibrated model is important in many applications, particularly when financial data […]

Read More

Categories

January 2018
MTWTFSS
« Dec Feb »
1234567
891011121314
15161718192021
22232425262728
293031