Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Management
|
| gptkbp:applies_to |
clustering problems
|
| gptkbp:can |
infinite mixture models
|
| gptkbp:has_applications_in |
gptkb:Genetics
gptkb:machine_learning image processing natural language processing |
| gptkbp:has_produced |
random probability measures
|
| https://www.w3.org/2000/01/rdf-schema#label |
Dirichlet process
|
| gptkbp:is_a |
gptkb:Dirichlet_distribution
|
| gptkbp:is_applied_in |
topic modeling
recommendation systems anomaly detection image segmentation |
| gptkbp:is_characterized_by |
uncertainty quantification
robustness to noise posterior distributions stick-breaking process ability to capture variability ability to handle overfitting ability to incorporate prior knowledge ability to learn from data ability to model heterogeneity adaptability to data base measure influence clustering behavior concentration parameter influence exchangeability flexibility in number of clusters flexible prior distributions infinite dimensionality modeling of complex data structures nonparametric nature random partitions randomness in partitions |
| gptkbp:is_defined_by |
Dirichlet process measure
|
| gptkbp:is_related_to |
Bayesian networks
nonparametric statistics Gaussian processes Markov chain Monte Carlo methods Dirichlet process mixture model |
| gptkbp:is_used_for |
density estimation
|
| gptkbp:is_used_in |
Bayesian inference
latent variable models hierarchical models |
| gptkbp:parameterized_by |
base measure
concentration parameter |
| gptkbp:provides |
flexibility in modeling
|
| gptkbp:related_to |
Chinese restaurant process
Polya urn scheme |
| gptkbp:used_in |
gptkb:Bayesian_nonparametrics
|
| gptkbp:bfsParent |
gptkb:Chinese_Restaurant_Process
|
| gptkbp:bfsLayer |
6
|