Hits: 175

# Laplace Distribution

The pdf of the Laplace distribution (aka the double exponential distribution) with location parameter μ and scale parameter β is

where β > 0. The cdf is

The inverse of the Laplace distribution is

Key statistical properties of the Laplace distribution are shown in Figure 1.

Figure 1 – Statistical properties of the Laplace distribution

Figure 2 shows a graph of the Laplace distribution for different values of μ and β.

Figure 2 – Chart of Laplace distribution

Real Statistics Functions: The Real Statistics Resource Pack provides the following functions for the Laplace distribution.

LAPLACE_DIST(x, μ, β, cum) = the pdf of the Laplace distribution f(x) when cum = FALSE and the corresponding cumulative distribution function F(x) when cum = TRUE.

LAPLACE_INV(p, μ, β) = the inverse of the Laplace distribution at p

Applied Data Science Coding: How to get class distribution in Data

# Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

## Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

# Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

`Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.`

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!