In this chapter, we talked about applying Bayesian learning in the case of learning parameters in HMMs. Bayesian learning has a few benefits over the maximum-likelihood estimator, but it turns out to be computationally quite expensive except when we have closed-form solutions. Closed-form solutions are only possible when we use conjugate priors. In the following chapters, we will discuss detailed applications of HMMs for a wide variety of problems.
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You're reading from Hands-On Markov Models with Python
Ankur Ankan is a BTech graduate from IIT (BHU), Varanasi. He is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. In his free time, he likes to participate in Kaggle competitions.
Read more about Ankur Ankan
Abinash Panda has been a data scientist for more than 4 years. He has worked at multiple early-stage start-ups and helped them build their data analytics pipelines. He loves to munge, plot, and analyze data. He has been a speaker at Python conferences. These days, he is busy co-founding a start-up. He has contributed to books on probabilistic graphical models by Packt Publishing.
Read more about Abinash Panda
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Ankur Ankan is a BTech graduate from IIT (BHU), Varanasi. He is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. In his free time, he likes to participate in Kaggle competitions.
Read more about Ankur Ankan
Abinash Panda has been a data scientist for more than 4 years. He has worked at multiple early-stage start-ups and helped them build their data analytics pipelines. He loves to munge, plot, and analyze data. He has been a speaker at Python conferences. These days, he is busy co-founding a start-up. He has contributed to books on probabilistic graphical models by Packt Publishing.
Read more about Abinash Panda