منابع مشابه
Bayesian Model Scoring in Markov Random Fields
Scoring structures of undirected graphical models by means of evaluating the marginal likelihood is very hard. The main reason is the presence of the partition function which is intractable to evaluate, let alone integrate over. We propose to approximate the marginal likelihood by employing two levels of approximation: we assume normality of the posterior (the Laplace approximation) and approxi...
متن کاملA Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring
A Bayesian latent variable model with classification and regression tree approach is built to overcome three challenges encountered by a bank in credit-granting process. These three challenges include (1) the bank wants to predict the future performance of an applicant accurately; (2) given current information about cardholders’ credit usage and repayment behavior, financial institutions would ...
متن کاملInflation Behavior in Top Sukuk Issuing Countries: Using a Bayesian Log-linear Model
This paper focused on developing a model to study the effect of sukuk issuance on the inflation rate in top sukuk issuing Islamic economies at 2014. For this purpose, as the available sample size is small, a Bayesian approach to regression model is used which contains key supply and demand side factors in addition to the outstanding sukuk volume as potential determinants of inflation rate...
متن کاملPAC-Bayesian AUC classification and scoring
We develop a scoring and classification procedure based on the PAC-Bayesian approach and the AUC (Area Under Curve) criterion. We focus initially on the class of linear score functions. We derive PAC-Bayesian non-asymptotic bounds for two types of prior for the score parameters: a Gaussian prior, and a spike-and-slab prior; the latter makes it possible to perform feature selection. One importan...
متن کاملScoring functions for learning Bayesian networks
The aim of this work is to benchmark scoring functions used by Bayesian network learning algorithms in the context of classification. We considered both information-theoretic scores, such as LL, AIC, BIC/MDL, NML and MIT, and Bayesian scores, such as K2, BD, BDe and BDeu. We tested the scores in a classification task by learning the optimal TAN classifier with benchmark datasets. We conclude th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Data Science
سال: 2021
ISSN: 1680-743X,1683-8602
DOI: 10.6339/jds.201307_11(3).0004