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Questions and Answers
What is a Bayesian network?
What is a Bayesian network?
- A probabilistic graphical model representing variables and their conditional dependencies using a directed acyclic graph (correct)
- A model representing variables and their unconditional dependencies using an undirected cyclic graph
- A deterministic mathematical model for solving decision problems
- A machine learning algorithm for unsupervised clustering
What is another name for a Bayesian network?
What is another name for a Bayesian network?
- Belief network (correct)
- Support vector machine
- Markov network
- Decision tree
Why are Bayesian networks considered probabilistic?
Why are Bayesian networks considered probabilistic?
- Because they always yield uncertain outcomes
- Because they are based on fuzzy logic
- Because they are built from a probability distribution and use probability theory for prediction and anomaly detection (correct)
- Because they rely on deterministic algorithms
What type of graph is used to represent a Bayesian network?
What type of graph is used to represent a Bayesian network?
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What is the generalized form of a Bayesian network for solving decision problems under uncertain knowledge?
What is the generalized form of a Bayesian network for solving decision problems under uncertain knowledge?
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Study Notes
Bayesian Networks
- A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
Alternative Name
- A Bayesian network is also known as a Bayes net or belief network.
Probabilistic Nature
- Bayesian networks are considered probabilistic because they model uncertainty using probability theory, allowing for the quantification of uncertainty and the updating of beliefs based on new evidence.
Graph Representation
- A Bayesian network is represented using a directed acyclic graph (DAG), which consists of nodes representing variables and edges representing conditional dependencies between variables.
Generalized Form for Decision-Making
- The generalized form of a Bayesian network for solving decision problems under uncertain knowledge is called an influence diagram, which extends the Bayesian network to include decision and utility nodes to model decision-making under uncertainty.
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Description
Test your knowledge about Bayesian belief networks, a key technology in artificial intelligence for handling probabilistic events and uncertainty. Learn about probabilistic graphical models, variable dependencies, and directed acyclic graphs.
