|
gptkbp:instance_of
|
gptkb:Statistician
|
|
gptkbp:has
|
gptkb:0
|
|
gptkbp:has_bell_shape
|
gptkb:true
|
|
gptkbp:has_cumulative_distribution_function
|
CDF(x) = 0.5 * (1 + erf(x / sqrt(2)))
|
|
gptkbp:has_property
|
mean = median = mode
|
|
gptkbp:has_standard_deviation
|
gptkb:1
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
The Gaussian distribution
|
|
gptkbp:integrates_with
|
statistical inference
|
|
gptkbp:is_a_central_figure_in
|
gptkb:central_limit_theorem
|
|
gptkbp:is_applicable_to
|
independent random variables
|
|
gptkbp:is_assumed_in
|
many statistical tests
|
|
gptkbp:is_characterized_by
|
mean and variance
68-95-99.7 rule
|
|
gptkbp:is_defined_by
|
gptkb:probability_density_function
|
|
gptkbp:is_described_as
|
two parameters
|
|
gptkbp:is_found_in
|
many natural phenomena
|
|
gptkbp:is_graphically_represented_by
|
bell curve
|
|
gptkbp:is_often_used_in
|
quality control
|
|
gptkbp:is_related_to
|
gptkb:Statistician
standard normal distribution
|
|
gptkbp:is_symmetric_around
|
mean
|
|
gptkbp:is_used_in
|
gptkb:sports_team
gptkb:market_research
gptkb:Artificial_Intelligence
gptkb:Genetics
gptkb:neural_networks
gptkb:machine_learning
gptkb:sociology
image processing
decision making
environmental science
finance
natural sciences
public health
statistics
climate science
education research
machine learning algorithms
operations research
psychometrics
regression analysis
risk assessment
time series analysis
data science
signal processing
social sciences
biostatistics
political science
safety engineering
marketing research
econometrics
forecasting
hypothesis testing
healthcare analytics
pharmaceutical research
|
|
gptkbp:known_as
|
gptkb:normal_distribution
|
|
gptkbp:model
|
real-valued random variables
|
|
gptkbp:bfsParent
|
gptkb:Louis_Gauss
gptkb:Karl_Friedrich_Gauss
|
|
gptkbp:bfsLayer
|
5
|