Bayesian Maximal Information Coefficient (BMIC) to reason novel trends in large datasets

Abstract

Bayesian network (BN) is a probability inference model to describe the explicit relationship of cause and effect, which may examine the complex system of rice price with data uncertainty. However, discovering the optimized structure from a super-exponential number of graphs in the search space is an NP-hard problem. In this paper, Bayesian maximal information coefficient (BMIC) is proposed to uncover the causal correlations from a large dataset in a random system by integrating probabilistic graphical model (PGM) and maximal information coefficient (MIC) with Bayesian linear regression (BLR). First, MIC is to capture the strong dependence between predictor variables and a target variable to reduce the number of variables for the BN structural learning of PGM. Second BLR is to assign orientation in a graph resulting by a posterior probability distribution. It conforms to what BN needs to acquire a conditional probability distribution when given the parents for each node by the Bayes' Theorem. Third, Bayesian information criterion (BIC) is treated as an indicator to determine the well-explained model with its data to ensure correctness. The score shows that the proposed method obtains the highest score compared to the two traditional learning algorithms. Finally, the BMIC is applied to discover the causal correlations from the large dataset on Thai rice price by identifying causality change in the paddy price of Jas-

Ensemble Modeling with a Bayesian Maximal Information Coefficient-Based Model of Bayesian Predictions on Uncertainty Data

“…Bayesian inference facilitates the quantification of uncertainty by providing probability distributions over parameters, leading to more nuanced and insightful predictions. The methodological framework of this study is presented in Figure 2 to describe the flow of the ensemble model, which is the integration of a traditional model with the BMIC model [4].…”

“…It is based on Bayes' theorem and encodes conditional dependencies between variables, allowing for efficient inference and reasoning under uncertainty. In [4], the authors applied the BN with the maximal information coefficient (MIC) to construct a good model as the Bayesian maximal information coefficient (BMIC) which demonstrates the clear relationship among features with the BIC score. However, it was found that a single model representation might present less accurate results in prediction.…”

“…According to Thai rice prices, it relates to uncertainty data, and many factors (features) can be affected. The model of the BMIC [4] presents a clear relationship model, which impacts predictive accuracy. Since it has been found that the statistics of the Bayesian approach can present the probability distribution of the model, in this study, we thus selected the model of the BMIC [4] which integrates various forms of knowledge of Bayes' theorem and the MIC to be the base model for incorporation with other traditional algorithms.…”

“…The model of the BMIC [4] presents a clear relationship model, which impacts predictive accuracy. Since it has been found that the statistics of the Bayesian approach can present the probability distribution of the model, in this study, we thus selected the model of the BMIC [4] which integrates various forms of knowledge of Bayes' theorem and the MIC to be the base model for incorporation with other traditional algorithms. Aggregation between different learning algorithms can extract new knowledge and reduce the occurrence of overfitting.…”

Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

“…The maximal information coefficient (MIC) method [54,55] can measure the strength of both linear and nonlinear associations between data, allowing for the discovery of diverse types of relationships among different types of load data. Therefore, in this study, the MIC is used to analyze the spatiotemporal characteristics of multiple systems and to construct a high-dimensional feature matrix for model inputs.…”