How Long to Read Introduction to Hidden Semi-Markov Models

By John Van der Hoek

How Long Does it Take to Read Introduction to Hidden Semi-Markov Models?

It takes the average reader 3 hours and 8 minutes to read Introduction to Hidden Semi-Markov Models by John Van der Hoek

Assuming a reading speed of 250 words per minute. Learn more

Description

Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications

How long is Introduction to Hidden Semi-Markov Models?

Introduction to Hidden Semi-Markov Models by John Van der Hoek is 185 pages long, and a total of 47,175 words.

This makes it 62% the length of the average book. It also has 58% more words than the average book.

How Long Does it Take to Read Introduction to Hidden Semi-Markov Models Aloud?

The average oral reading speed is 183 words per minute. This means it takes 4 hours and 17 minutes to read Introduction to Hidden Semi-Markov Models aloud.

What Reading Level is Introduction to Hidden Semi-Markov Models?

Introduction to Hidden Semi-Markov Models is suitable for students ages 10 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy Introduction to Hidden Semi-Markov Models?

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