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Are Bayesian networks still relevant?
Bayesian networks are arguably more successful than deep neural networks to date. They are widely applied across industries and can readily be used for inference, modelling, and prediction.
What are the pros and cons of neural network?
Pros and cons of neural networks
- Neural networks are flexible and can be used for both regression and classification problems.
- Neural networks are good to model with nonlinear data with large number of inputs; for example, images.
- Once trained, the predictions are pretty fast.
What are the advantages and disadvantages of Bayesian learning?
With all of these advantages, Bayesian learning is a strong program. However, there are also some very significant disadvantages. Information theoretically infeasible It turns out that specifying a prior is extremely difficult.
What are the limitations of Bayesian network ( BN )?
Bayesian network (BN) is directed graphical model where the directed edges are used to capture the casual relationships between random variables. BN cannot be used to model the correlation relationships between random variables. That’s why undirected graphical models (Markov Random Field) kicks in.
Can a Bayesian network deal with continuous random variables?
As far as “Can Bayesian networks deal with…data that is not easily discretized…”, you can certainly include continuous random variables in your network. If you know the distribution and it’s a simple one (e.g., Gaussian), then you can use some form of belief propagation.
How are Bayesian networks used in medical systems?
Bayesian networks (BN) have been used to build medical diagnostic systems. The networks are hand-built by medical experts and later used to infer likelihood of different causes given observed symptoms. Similar systems have also been built for diagnosing problems in factories and other mechanical systems.