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Book Synopsis

- 1. Overview.- Part I: Fitting Likelihood and Bayesian Models.- 2. Likelihood.- 3. Computing the Likelihood.- 4. Bayesian Methods.- 5. McMC in Practice.- Part II: Prediction.- 6. Fundamentals of Prediction.- 7. Shrinkage Methods.- 8. Digression on Multiple Testing: False Discovery Rates.- 9. Binary Data.- 10. Bayesian Prediction and Model Checking.- 11. Nonparametric Methods: A Selected Overview.- Part III: Exercises and Solutions.- 12. Exercises.- 13. Solution to Exercises.

Statistical Learning in Genetics

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    Order before 4pm tomorrow for delivery by Tue 16 Jun 2026.

    A Hardback by Daniel Sorensen

    15 in stock


      View other formats and editions of Statistical Learning in Genetics by Daniel Sorensen

      Publisher: Springer
      Publication Date: 4/29/2025
      ISBN13: 9783031862731, 978-3031862731
      ISBN10: 3031862732
      Also in:
      Probability and statistics

      Description

      Book Synopsis

      - 1. Overview.- Part I: Fitting Likelihood and Bayesian Models.- 2. Likelihood.- 3. Computing the Likelihood.- 4. Bayesian Methods.- 5. McMC in Practice.- Part II: Prediction.- 6. Fundamentals of Prediction.- 7. Shrinkage Methods.- 8. Digression on Multiple Testing: False Discovery Rates.- 9. Binary Data.- 10. Bayesian Prediction and Model Checking.- 11. Nonparametric Methods: A Selected Overview.- Part III: Exercises and Solutions.- 12. Exercises.- 13. Solution to Exercises.

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