Graphical models are a useful tool in statistics that help us understand connections between different variables in complex data. They can be used in biology, social science, and other fields to analyze relationships and patterns. By combining graphical models with regression and classification techniques, we can make predictions and classify data accurately. This paper reviews how graphical models are estimated and the algorithms used for computation. It also explores advanced topics like complex structures and noisy data. Overall, graphical models are a powerful tool for analyzing data and making sense of relationships between variables.