Probabilistic Graphical Models: Principles and Techniques
E298583
Probabilistic Graphical Models: Principles and Techniques is a foundational textbook that systematically presents the theory, algorithms, and applications of probabilistic graphical models in machine learning and artificial intelligence.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Probabilistic Graphical Models: Principles and Techniques canonical | 2 |
| Probabilistic Graphical Models | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2790873 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Probabilistic Graphical Models: Principles and Techniques Context triple: [Daphne Koller, notableWork, Probabilistic Graphical Models: Principles and Techniques]
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A.
Bayesian networks
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
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B.
Markov random fields
Markov random fields are probabilistic graphical models that represent the joint distribution of a set of random variables with local dependencies encoded by an undirected graph, widely used in areas like statistical physics, computer vision, and spatial statistics.
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C.
Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
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D.
Bayesian Occam factor
The Bayesian Occam factor is a term in Bayesian model comparison that automatically penalizes overly complex models by integrating over their larger parameter spaces, thereby implementing Occam’s razor in probabilistic inference.
-
E.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Probabilistic Graphical Models: Principles and Techniques Target entity description: Probabilistic Graphical Models: Principles and Techniques is a foundational textbook that systematically presents the theory, algorithms, and applications of probabilistic graphical models in machine learning and artificial intelligence.
-
A.
Bayesian networks
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
-
B.
Markov random fields
Markov random fields are probabilistic graphical models that represent the joint distribution of a set of random variables with local dependencies encoded by an undirected graph, widely used in areas like statistical physics, computer vision, and spatial statistics.
-
C.
Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
-
D.
Bayesian Occam factor
The Bayesian Occam factor is a term in Bayesian model comparison that automatically penalizes overly complex models by integrating over their larger parameter spaces, thereby implementing Occam’s razor in probabilistic inference.
-
E.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
academic book
ⓘ
artificial intelligence textbook ⓘ machine learning textbook ⓘ non-fiction book ⓘ textbook ⓘ |
| academicDiscipline |
applied mathematics
ⓘ
computer science ⓘ |
| author |
Daphne Koller
ⓘ
Nir Friedman ⓘ |
| countryOfPublication |
United States
ⓘ
linked to:
United States of America
|
| field |
artificial intelligence
ⓘ
machine learning ⓘ probabilistic graphical models ⓘ statistics ⓘ |
| focus |
algorithms for probabilistic graphical models
ⓘ
applications of probabilistic graphical models ⓘ theory of probabilistic graphical models ⓘ |
| intendedAudience |
graduate students
ⓘ
practitioners in machine learning ⓘ researchers ⓘ |
| language | English ⓘ |
| publisher | MIT Press ⓘ |
| shortTitle | Probabilistic Graphical Models ⓘ |
| title | Probabilistic Graphical Models: Principles and Techniques ⓘ |
| topic |
Bayesian networks
ⓘ
Markov chain Monte Carlo ⓘ Markov networks ⓘ
linked to:
Markov random fields
Monte Carlo methods ⓘ approximate inference ⓘ belief propagation ⓘ conditional independence ⓘ decision theory ⓘ directed graphical models ⓘ dynamic Bayesian networks ⓘ exact inference ⓘ exponential family distributions ⓘ factor graphs ⓘ graphical model representation ⓘ inference algorithms ⓘ influence diagrams ⓘ latent variable models ⓘ loopy belief propagation ⓘ parameter learning ⓘ structure learning ⓘ temporal models ⓘ undirected graphical models ⓘ variational inference ⓘ |
| usedAs |
graduate-level textbook
ⓘ
reference book ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Probabilistic Graphical Models: Principles and Techniques Description of subject: Probabilistic Graphical Models: Principles and Techniques is a foundational textbook that systematically presents the theory, algorithms, and applications of probabilistic graphical models in machine learning and artificial intelligence.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
Probabilistic Graphical Models: Principles and Techniques
→
title
→
Probabilistic Graphical Models: Principles and Techniques
ⓘ
Probabilistic Graphical Models: Principles and Techniques
→
shortTitle
→
Probabilistic Graphical Models
ⓘ
linked to: Probabilistic Graphical Models: Principles and Techniques