- Published on October 8, 2021
- In Deep Tech
A Guide to Markov Chain and its Applications in Machine Learning
A stochastic process can be considered as the Markov chain if the process consists of the Markovian properties which are to process the future.
- By Yugesh Verma
Markov Chains are one of the simple and very useful tools in order to model time-dependent, space-dependent stochastic processes. Many domains like finance (stock price movement), sales(sales quantity information), NLP algorithms (finite-state transducers, Hidden Markov Model for POS Tagging), weather forecasting, etc use the Markov chain to make their predictions easily and accurately. In this article, we will discuss the Markovian Chains in detail. We will try to understand their working with advantages and applications. The major points to be covered in this article are listed below.
Table of Contents
DefinitionsState-spaceTrajectory Prediction Using Markov ChainInitial State and One-Step PredictionLong Run PredictionAdvantages of Markov ChainApplication of the Markov Chain
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Yugesh Verma
Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.
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