Statements (117)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Management
|
| gptkbp:analyzes |
a restaurant with tables and customers
|
| gptkbp:can_be_extended_by |
Chinese restaurant franchise model
|
| gptkbp:can_create |
infinite mixtures of distributions
|
| gptkbp:concept |
gptkb:Ethics
gptkb:sociology information theory cultural studies network theory team building knowledge management strategic management organizational theory statistical learning theory computational statistics |
| gptkbp:describes |
a distribution over partitions of a set
|
| gptkbp:has_applications_in |
topic modeling
|
| https://www.w3.org/2000/01/rdf-schema#label |
Chinese Restaurant Process
|
| gptkbp:introduced_in |
A. P. D. G. Teh
|
| gptkbp:is_a_basis_for |
collaborative filtering
|
| gptkbp:is_a_foundation_for |
gptkb:Statistician
|
| gptkbp:is_a_framework_for |
data analysis
quantitative research strategic planning organizational strategy unsupervised learning statistical inference organizational development organizational effectiveness talent management socioeconomic research dynamic modeling of data |
| gptkbp:is_a_probabilistic_model_for |
grouping data points
|
| gptkbp:is_a_tool_for |
predictive modeling
policy analysis anomaly detection performance management strategic alignment data exploration |
| gptkbp:is_analyzed_in |
Bayesian inference
market trends social interactions organizational performance |
| gptkbp:is_applicable_to |
gptkb:urban_development
change management financial modeling bioinformatics social network analysis organizational learning |
| gptkbp:is_applied_in |
gptkb:machine_learning
image segmentation |
| gptkbp:is_characterized_by |
the Chinese restaurant metaphor
|
| gptkbp:is_connected_to |
hierarchical models
|
| gptkbp:is_defined_by |
a parameter alpha
|
| gptkbp:is_explored_in |
gptkb:architecture
|
| gptkbp:is_implemented_in |
Markov chain Monte Carlo methods
|
| gptkbp:is_often_compared_to |
K-means clustering
|
| gptkbp:is_related_to |
gptkb:Dirichlet_process
latent variable models stick-breaking process |
| gptkbp:is_relevant_to |
gptkb:psychology
economics public health corporate governance education research workplace diversity |
| gptkbp:is_similar_to |
Polya urn model
|
| gptkbp:is_studied_for |
employee engagement
population dynamics network dynamics |
| gptkbp:is_used_for |
data clustering
|
| gptkbp:is_used_in |
gptkb:Bayesian_nonparametrics
gptkb:Artificial_Intelligence gptkb:Genetics conflict resolution environmental studies human resources management corporate social responsibility. |
| gptkbp:is_used_to_infer |
the number of clusters in data
|
| gptkbp:is_useful_in |
recommendation systems
|
| gptkbp:key_concept |
machine learning theory
|
| gptkbp:model |
gptkb:collaboration
gptkb:economic_analysis gptkb:supply_chain_management decision making organizational behavior organizational culture team dynamics customer behavior data visualization leadership development social networks sustainability practices random graphs customer segmentation complex systems organizational change business transformation dynamic systems innovation management community dynamics workplace behavior social phenomena behavioral modeling non-exchangeable partitions the allocation of customers to tables |
| gptkbp:technique |
risk assessment
stakeholder engagement data mining pattern recognition leadership studies analyzing time series data |
| gptkbp:type_of |
nonparametric Bayesian model
random partition model |
| gptkbp:bfsParent |
gptkb:The_Hierarchical_Dirichlet_Process
gptkb:Bayesian_Nonparametric_Topic_Models |
| gptkbp:bfsLayer |
5
|