Bayesian Networks
Bayesian Networks are probabilistic graphical models that represent a set of variables and their conditional dependencies via a directed acyclic graph (DAG). They are used for reasoning under uncertainty, allowing efficient computation of probabilities for inference, prediction, and decision-making. These networks combine principles from probability theory and graph theory to model complex systems in fields like artificial intelligence, medicine, and finance.
Developers should learn Bayesian Networks when building systems that require probabilistic reasoning, such as diagnostic tools, risk assessment models, or recommendation engines. They are particularly useful in AI applications like spam filtering, medical diagnosis, and autonomous systems where uncertainty and causal relationships must be quantified. Mastering this concept enhances skills in machine learning, data science, and decision support systems.