A Median-Based Machine-Learning Approach for Predicting Random Sampling Bernoulli Distribution Parameter
In real-life applications, we often do not have population data but we can collect several samples from a large sample size of data. In this paper, we propose a median-based machine-learning approach and algorithm to predict the parameter of the Bernoulli distribution. We illustrate the proposed med...
Main Authors: | Hoang Pham, David H. Pham |
---|---|
Format: | Article |
Language: | English |
Published: |
World Scientific Publishing
2019-02-01
|
Series: | Vietnam Journal of Computer Science |
Subjects: | |
Online Access: | http://www.worldscientific.com/doi/pdf/10.1142/S2196888819500015 |
Similar Items
-
Admissible Bernoulli correlations
by: Mark Huber, et al.
Published: (2019-03-01) -
Computing the asymptotic expansion of the median of the erlang distribution
by: Pedro Jodr´a
Published: (2012-04-01) -
Improved Bernoulli Sampling for Discrete Gaussian Distributions over the Integers
by: Shaohao Xie, et al.
Published: (2021-02-01) -
Inner pressure prediction of suction cup based on Bernoulli equation
by: Yang Tian, et al.
Published: (2021-12-01) -
Sufficient Condition for Monotonicity in Constructing the Distribution Function With Bernoulli Scheme
by: Vedenyapin Aleksandr Dmitrievich, et al.
Published: (2015-11-01)