  The `Jacobi prior' is an alternative Bayesian method for predictive models.
It performs better than well-known methods such as Lasso, Ridge, Elastic Net,
and MCMC-based Horse-Shoe Prior, particularly in terms of prediction accuracy
and run-time. This method is implemented for Gaussian process classification,
adeptly handling a nonlinear decision boundary. The Jacobi prior demonstrates
its capability to manage partitioned data across global servers, making it
highly useful in distributed computing environments. Additionally, we show that
the Jacobi prior is more than a hundred times faster than these methods while
maintaining similar predictive accuracy. As the method is both fast and
accurate, it is advantageous for organisations looking to reduce their
environmental impact and meet ESG standards. To demonstrate the effectiveness
of the Jacobi prior, we conducted a detailed simulation study with four
experiments focusing on statistical consistency, accuracy, and speed. We also
present two empirical studies: the first evaluates credit risk by analysing
default probability using data from the U.S. Small Business Administration
(SBA), and the second uses the Jacobi prior for classifying stars, quasars, and
galaxies in a three-class problem using multinomial logit regression on data
from the Sloan Digital Sky Survey. Different filters were used as features in
this study. All codes and datasets for this paper are available in the
following GitHub repository : https://github.com/sourish-cmi/Jacobi-Prior/
