Bayesian Networks
What are Bayesian networks?
Bayesian network, popularly known as the Bayesian model, is a technology that deals with probabilistic events and solves a problem that has uncertainty. It is a graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph. Bayesian networks are usually probabilistic and use probability theory for prediction and anomaly detection. It is used to represent the relationship between multiple events. It is used in various applications such as prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction, and decision making under uncertainty.
What are the benefits of Bayesian networks?
- Graphical and visual networks provide a model to visualize the structure of the probabilities and develop designs for new models.
- It determines the type of relationship and the presence or absence of it between variables.
- Computations calculate complex probability problems efficiently.
- It can investigate and tell you whether a particular feature is taken into a note for the decision-making process and can coerce it to include that feature if necessary. It ensures that all known features are investigated for deciding on a problem.
- They are more extensible than other networks and learning methods. Adding a new piece in the network requires only a few probabilities and edges in the graph. Hence, it is an excellent network for adding new data to an existing probabilistic model.
- Both computers and humans can interpret the information, unlike some networks like neural networks, which humans can’t read.
