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Published byRoy Booker Modified over 5 years ago
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Learning Probabilistic Graphical Models Overview Learning Problems
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Learning Data Network dataset of instances D={d[1],...d[m]}
domain expert Declarative representation Network Learning elicitation
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Learning Tasks Known structure Unknown structure Latent variables
Fully observable data Partly observable data
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Known Structure, Complete Data
X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y x10 x21 y0 x11 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data
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Unknown Structure, Complete Data
X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y x10 x21 y0 x11 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data
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Known Structure, Incomplete Data
X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y ? x21 y0 x11 x10 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data
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Unknown Structure, Incomplete Data
X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y ? x21 y0 x11 x10 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data
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Latent Variables, Incomplete Data
H X1 X2 X1 X2 Inducer Initial network Y Y X1 X2 Y ? x21 y0 x11 x10 x20 y1 P(Y|X1,X2) X1 X2 y0 y1 x10 x20 1 x21 0.2 0.8 x11 0.1 0.9 0.02 0.98 Input Data
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Learning Tasks: BNs Known structure Unknown structure Latent variables
Fully observable data Partly observable data
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Learning Tasks: MNs Known structure Unknown structure Latent variables
Fully observable data Partly observable data
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Target Tasks Probabilistic queries about new instances
Specific tasks (e.g., classification) Knowledge discovery Direct vs indirect dependencies Possibly directionality of edges Directionality of influence Hidden variables
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Performance Metrics Distance of learned model to true distribution
Evaluate performance by how well the network estimates probability of new examples (“test data”) Task-specific accuracy on test set Distance of learned structure or parameters to true a ground truth model Often compare to (limited) prior knowledge
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