The Human Development Index (HDI) is a widely used measure of development based on health, education, and income indicators published by UNDP. Although HDI is easy to interpret, it is calculated using a fixed formula that does not show uncertainty or explain how these indicators influence each other. With the availability of long-term HDI data for many countries, more flexible statistical methods can be applied.

In this study, we use a Bayesian Network to model the relationships between HDI and its core components: life expectancy, expected years of schooling, mean years of schooling, and gross national income per capita. A Bayesian Network represents these indicators as connected variables, where the connections are learned from UNDP data rather than assumed in advance. The model is estimated using standard Bayesian statistical methods and validated using cross-country data.

The results are expected to show that HDI components are not independent and education plays a crucial role in influencing both income and health outcomes. The Bayesian Network allows us to estimate the probability distribution of HDI rather than a single value, revealing uncertainty in HDI scores and rankings. Policy simulation experiments demonstrate that improvements in different indicators lead to different HDI gains depending on a country’s development structure.

This probabilistic framework extends the traditional HDI by incorporating uncertainty and interdependence among indicators. The proposed approach provides a useful statistical tool for development analysis and policy evaluation using official UNDP data.