A Gentle Introduction to Bayesian Belief Networks
Probabilistic models can define relationships between variables and be used to calculate probabilities.
For example, fully conditional models may require an enormous amount of data to cover all possible cases, and probabilities may be intractable to calculate in practice. Simplifying assumptions such as the conditional independence of all random variables can be effective, such as in the case of Naive Bayes, although it is a drastically simplifying step.
An alternative is to develop a model that preserves known conditional dependence between random variables and conditional independence in all other cases. Bayesian networks are a probabilistic graphical model that explicitly capture the known conditional dependence with directed edges in a graph model. All missing connections define the conditional independencies in the model.
As such Bayesian Networks provide a useful tool to visualize the probabilistic model for a domain, review all of the relationships between the random variables, and reason about causal probabilities for scenarios given available evidence.
In this post, you will discover a gentle introduction to Bayesian Networks.
After reading this post, you will know:
- Bayesian networks are a type of probabilistic graphical model comprised of nodes and directed edges.
- Bayesian network models capture both conditionally dependent and conditionally
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