Log Predictive Density

Log Predictive Density is the logarithm of the predictive density evaluated at observed data points, serving as a measure of a model’s out-of-sample predictive accuracy by quantifying the probability density assigned to the actual data under the model’s posterior predictive distribution.

25

Publications

752

Citations

47

Authors

51

Institutions

Publications per year

2017–2026

6

Authors

47

  1. NR
    Norman R. Swanson

    Rutgers, The State University of New Jersey, United States

    4 publications · 203 citations

    4

    h-index

  2. VC
    Valentina Corradi

    University of Calgary, Canada

    3 publications · 94 citations

    3

    h-index

  3. WE
    William E. Strawderman

    Rutgers, The State University of New Jersey, United States

    2 publications · 27 citations

    2

    h-index

  4. ÉM
    Éric Marchand

    Université de Sherbrooke, Canada

    2 publications · 27 citations

    2

    h-index

  5. LP
    Liam Paninski

    Columbia University, United States

    1 publications · 16 citations

    1

    h-index

1–5 of 47

Venues

  1. SSRN Electronic Journal

    Repository · 4 publications · 142 citations

    4

    h-index

  2. Journal of Forecasting

    2 publications · 47 citations

    2

    h-index

  3. 2

    h-index

  4. Journal of Econometrics

    2 publications · 127 citations

    2

    h-index

  5. Journal of Multivariate Analysis

    2 publications · 28 citations

    2

    h-index

1–5