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t-distribution
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Package provides the javascript implementation of various statistics and distribution
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Updated
May 31, 2017 - JavaScript
Replication code for simulating and estimation by GMM of DSGE models with higher-order statistics
nonlinear
gaussian
pruning
gmm
dsge
power-spectrum
spectral-density-estimates
dsge-models
t-distribution
perturbation
bispectrum
bispectrum-computation
skewness
kurtosis
rbc-model
trispectrum
trispectrum-computation
autocovariogram
higher-order-statistics
nonlinear-estimation
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Jul 19, 2018 - MATLAB
Replication code for checking identification in nonlinear pruned DSGE models with Gaussian or Student's t distributed errors
nonlinear
gaussian
identification
pruning
dsge
power-spectrum
spectral-density-estimates
dsge-models
t-distribution
perturbation
bispectrum
bispectrum-computation
skewness
kurtosis
trispectrum
trispectrum-computation
autocovariogram
higher-order-statistics
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Updated
Jul 20, 2018 - MATLAB
A comparative study between 5 different binary classification techniques.
python
opencv
computer-vision
gaussian-mixture-models
expectation-maximization-algorithm
factor-analysis
gaussian-distribution
t-distribution
face-classifier
image-classification-algorithms
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Jul 7, 2018 - Python
Background for Hypothesis testing / Bayesian Inference with code examples
examples
scipy
bayesian-inference
hypothesis-testing
normal-distribution
binomial-distribution
beta-distribution
t-distribution
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Feb 20, 2019 - Jupyter Notebook
My notes on statistics
statistics
statistical-methods
mean
mode
hypothesis-testing
median
central-limit-theorem
normal-distribution
t-distribution
confidence-interval
t-statistic
central-tendency
z-statistic
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Dec 8, 2019 - Jupyter Notebook
Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS
clustering
statistical-learning
prediction
artificial-intelligence
statistical-inference
neural-networks
skew-t
unsupervised-learning
em-algorithm
non-linear-regression
t-distribution
mixture-of-experts
skewed-data
robust-learning
skew-normal
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Updated
Feb 13, 2020 - R
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The code for the Gamma distribution is very incomplete -- the class only basically only contains code for random number generation from a Gamma distribution.
I implemented the pdf, cdf, icdf as well as unit tests, and noticed that the parameters are named $shape and $rate, which would seem congruent with alpha and beta as described in [Wikipedia's](https://en.wikipedia.org/wiki/Gamma_distributi