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Filtering and Prediction: A Primer
 
B. Fristedt University of Minnesota, Minneapolis, MN
N. Jain University of Minnesota, Minneapolis, MN
N. Krylov University of Minnesota, Minneapolis, MN
Filtering and Prediction: A Primer
Softcover ISBN:  978-0-8218-4333-8
Product Code:  STML/38
List Price: $59.00
Individual Price: $47.20
eBook ISBN:  978-1-4704-1216-6
Product Code:  STML/38.E
List Price: $49.00
Individual Price: $39.20
Softcover ISBN:  978-0-8218-4333-8
eBook: ISBN:  978-1-4704-1216-6
Product Code:  STML/38.B
List Price: $108.00 $83.50
Filtering and Prediction: A Primer
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Filtering and Prediction: A Primer
B. Fristedt University of Minnesota, Minneapolis, MN
N. Jain University of Minnesota, Minneapolis, MN
N. Krylov University of Minnesota, Minneapolis, MN
Softcover ISBN:  978-0-8218-4333-8
Product Code:  STML/38
List Price: $59.00
Individual Price: $47.20
eBook ISBN:  978-1-4704-1216-6
Product Code:  STML/38.E
List Price: $49.00
Individual Price: $39.20
Softcover ISBN:  978-0-8218-4333-8
eBook ISBN:  978-1-4704-1216-6
Product Code:  STML/38.B
List Price: $108.00 $83.50
  • Book Details
     
     
    Student Mathematical Library
    Volume: 382007; 252 pp
    MSC: Primary 60;

    Filtering and prediction is about observing moving objects when the observations are corrupted by random errors. The main focus is then on filtering out the errors and extracting from the observations the most precise information about the object, which itself may or may not be moving in a somewhat random fashion. Next comes the prediction step where, using information about the past behavior of the object, one tries to predict its future path.

    The first three chapters of the book deal with discrete probability spaces, random variables, conditioning, Markov chains, and filtering of discrete Markov chains. The next three chapters deal with the more sophisticated notions of conditioning in nondiscrete situations, filtering of continuous-space Markov chains, and of Wiener process. Filtering and prediction of stationary sequences is discussed in the last two chapters.

    The authors believe that they have succeeded in presenting necessary ideas in an elementary manner without sacrificing the rigor too much. Such rigorous treatment is lacking at this level in the literature. In the past few years the material in the book was offered as a one-semester undergraduate/beginning graduate course at the University of Minnesota. Some of the many problems suggested in the text were used in homework assignments.

    Readership

    Undergraduate and graduate students interested in filtering and prediction for random processes.

  • Table of Contents
     
     
    • Chapters
    • Chapter 1. Preliminaries
    • Chapter 2. Markov chains
    • Chapter 3. Filtering of discrete Markov chains
    • Chapter 4. Conditional expectations
    • Chapter 5. Filtering of continuous-space Markov chains
    • Chapter 6. Wiener process and continuous time filtering
    • Chapter 7. Stationary sequences
    • Chapter 8. Prediction of stationary sequences
  • Reviews
     
     
    • The book is written in an elementary way but it is still mathematically rigorous. The book can be recommended to all students interested in stochastic models.

      EMS Newsletter
    • The book is well-written and provides a very nice basis for lecturing about this topic.

      Zentralblatt MATH
  • Requests
     
     
    Review Copy – for publishers of book reviews
    Desk Copy – for instructors who have adopted an AMS textbook for a course
    Examination Copy – for faculty considering an AMS textbook for a course
    Permission – for use of book, eBook, or Journal content
    Accessibility – to request an alternate format of an AMS title
Volume: 382007; 252 pp
MSC: Primary 60;

Filtering and prediction is about observing moving objects when the observations are corrupted by random errors. The main focus is then on filtering out the errors and extracting from the observations the most precise information about the object, which itself may or may not be moving in a somewhat random fashion. Next comes the prediction step where, using information about the past behavior of the object, one tries to predict its future path.

The first three chapters of the book deal with discrete probability spaces, random variables, conditioning, Markov chains, and filtering of discrete Markov chains. The next three chapters deal with the more sophisticated notions of conditioning in nondiscrete situations, filtering of continuous-space Markov chains, and of Wiener process. Filtering and prediction of stationary sequences is discussed in the last two chapters.

The authors believe that they have succeeded in presenting necessary ideas in an elementary manner without sacrificing the rigor too much. Such rigorous treatment is lacking at this level in the literature. In the past few years the material in the book was offered as a one-semester undergraduate/beginning graduate course at the University of Minnesota. Some of the many problems suggested in the text were used in homework assignments.

Readership

Undergraduate and graduate students interested in filtering and prediction for random processes.

  • Chapters
  • Chapter 1. Preliminaries
  • Chapter 2. Markov chains
  • Chapter 3. Filtering of discrete Markov chains
  • Chapter 4. Conditional expectations
  • Chapter 5. Filtering of continuous-space Markov chains
  • Chapter 6. Wiener process and continuous time filtering
  • Chapter 7. Stationary sequences
  • Chapter 8. Prediction of stationary sequences
  • The book is written in an elementary way but it is still mathematically rigorous. The book can be recommended to all students interested in stochastic models.

    EMS Newsletter
  • The book is well-written and provides a very nice basis for lecturing about this topic.

    Zentralblatt MATH
Review Copy – for publishers of book reviews
Desk Copy – for instructors who have adopted an AMS textbook for a course
Examination Copy – for faculty considering an AMS textbook for a course
Permission – for use of book, eBook, or Journal content
Accessibility – to request an alternate format of an AMS title
Please select which format for which you are requesting permissions.