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gptkbp:instance_of
|
gptkb:analysis
|
|
gptkbp:advocated_for
|
Data Scientists
Machine Learning Practitioners
Bayesian Statisticians
|
|
gptkbp:applies_to
|
gptkb:Data_Science
|
|
gptkbp:focuses_on
|
Bayesian Inference
|
|
gptkbp:has_written
|
gptkb:Andrew_Gelman
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Bayesian Data Analysis
|
|
gptkbp:includes
|
Prior Distributions
Likelihood Functions
Posterior Distributions
|
|
gptkbp:is_applied_in
|
gptkb:psychology
Biostatistics
Economics
Epidemiology
|
|
gptkbp:is_associated_with
|
gptkb:strategy
Decision Theory
Information Theory
|
|
gptkbp:is_challenged_by
|
Non-Bayesian Methods
|
|
gptkbp:is_considered_as
|
A Paradigm Shift
A Framework for Inference
A Methodology for Learning
|
|
gptkbp:is_criticized_for
|
Complexity
Subjectivity
Computational Intensity
|
|
gptkbp:is_described_as
|
gptkb:Workshops
Online Courses
Textbooks
|
|
gptkbp:is_discussed_in
|
Statistical Literature
|
|
gptkbp:is_enhanced_by
|
Software Tools
Python Libraries
R Packages
|
|
gptkbp:is_examined_in
|
Research Papers
|
|
gptkbp:is_influenced_by
|
Frequentist Statistics
Bayesian Probability
|
|
gptkbp:is_part_of
|
Bayesian Statistics
|
|
gptkbp:is_popular_in
|
Social Sciences
|
|
gptkbp:is_promoted_by
|
Research Institutions
Academic Conferences
Statistical Societies
|
|
gptkbp:is_related_to
|
gptkb:Markov_Chain_Monte_Carlo
|
|
gptkbp:is_supported_by
|
Computational Methods
Bayesian Networks
Empirical Bayes
Hierarchical Models
|
|
gptkbp:is_taught_in
|
Statistics Courses
|
|
gptkbp:is_used_for
|
Hypothesis Testing
Predictive Modeling
Parameter Estimation
Model Comparison
|
|
gptkbp:is_used_in
|
gptkb:sports_team
gptkb:Artificial_Intelligence
gptkb:advertising
gptkb:machine_learning
Finance
|
|
gptkbp:published_in
|
gptkb:2003
|
|
gptkbp:uses
|
Bayes' Theorem
|
|
gptkbp:bfsParent
|
gptkb:Alan_B._Gelfand
gptkb:The_Elements_of_Statistical_Learning:_Data_Mining,_Inference,_and_Prediction
gptkb:Philip_Stark
|
|
gptkbp:bfsLayer
|
6
|