Bayesian Networks, also known as Belief Networks or Bayes Nets, are a class of statistical models that use graph theory to represent and solve problems in a wide range of fields including machine learning, statistics, and artificial intelligence. Each node in the graph represents a random variable, while the edges between these nodes denote conditional dependencies. By quantifying the relationships between variables, Bayesian Networks enable the computation of probabilities for a set of variables, facilitating both inference and decision-making processes under uncertainty. They are particularly useful for tasks such as diagnostic problem solving, risk analysis, and prediction, offering a structured way to incorporate prior knowledge and observed data.

Historical overview: The concept of Bayesian Networks was first introduced in the late 20th century, gaining prominence in the 1980s through the pioneering work of Judea Pearl and others. Their development marked a significant advancement in the fields of probabilistic reasoning and machine learning, providing a framework for modeling complex systems.

Key contributors: Judea Pearl is a key figure in the development of Bayesian Networks, having introduced and developed the theoretical foundations that underlie these models. His contributions have been instrumental in establishing Bayesian Networks as a critical tool in probabilistic reasoning and decision-making processes.