An empirical distribution function provides a way to model and sample cumulative probabilities for a data sample that does not…

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## A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically,…

Continue Reading## Fat tails and the t test

The t statistic is where y bar is the sample average, μ0 is the mean under the null hypothesis (μ0…

Continue Reading## A Gentle Introduction to Probability Density Estimation

Probability density is the relationship between observations and their probability. Some outcomes of a random variable will have low probability…

Continue Reading## Continuous Probability Distributions for Machine Learning

The probability for a continuous random variable can be summarized with a continuous probability distribution. Continuous probability distributions are encountered…

Continue Reading## Interview: Terry Deem and David Liu at Intel

Terry Deem: The Intel® Distribution for Python is best suited for the needs of data scientists, data engineers, deep learning…

Continue Reading## Supercharge Data Science Applications with the Intel® Distribution for Python

Sponsored Post The Python language plays a prominent role in almost every data scientist’s workflow. There are countless easy-to-use Python…

Continue Reading## Poisson Distribution Intuition (and derivation)

Poisson Distribution Intuition (and derivation)When to use a Poisson Distribution?Aerin Kim ????BlockedUnblockFollowFollowingJun 1Before setting the parameter λ and plugging it…

Continue Reading## Bayesian inference problem, MCMC and variational inference

Bayesian inference problem, MCMC and variational inferenceOverview of the Bayesian inference problem in statistics. Joseph RoccaBlockedUnblockFollowFollowingJul 1Credit: Free-Photos on PixabayThis post…

Continue Reading## Basics of Independent Component Analysis

From a visual perspective, it feels pretty clear that there are two populations with two linear trends. The two groups…

Continue Reading## Ever Wondered Why Normal Distribution Is So Important?

What is the logic behind it?The idea revolves around the theorem that when you repeat an experiment a large number of…

Continue Reading## How GANs really work

Imagine we are at equilibrium and the generator is not sampling on the underlying distribution of X (ie the distribution…

Continue Reading## Behind The Models: Dirichlet — How Does It Add To 1?

Behind The Models: Dirichlet — How Does It Add To 1?Building Blocks For Non-Parametric Bayesian ModelsTony PistilliBlockedUnblockFollowFollowingJun 18In a previous article I presented the…

Continue Reading## Understanding Gaussian Classifier

This leads to a multivariate normal distribution, the equation of which is given below:Σ is a covariance matrix. Function symbol…

Continue Reading## What I learned in RSNA Radiology in the Age of AI Spotlight Course

How clean are the data?Those are some interesting ideas to think about, also they have developed a level of data…

Continue Reading## Credit Spread In Finance And Their Probability Distributions In Data Science

Credit Spread In Finance And Their Probability Distributions In Data ScienceUsing Python To Demonstrate Financial Credit Spreads And Hazard RatesFarhad MalikBlockedUnblockFollowFollowingJun 11One…

Continue Reading## After raw stats: exploring possession styles with data embeddings.

To keep it simple: it’s a flow of passes and moves until the team having the ball lose it. So…

Continue Reading## PCA Factors most sensitive to distributional changes

PCA Factors most sensitive to distributional changesVivek PalaniappanBlockedUnblockFollowFollowingJun 5This article is a summary and exploration of the research paper “Which…

Continue Reading## MCMC Intuition for Everyone

Can you think of a way?Think…. MCMC provides us with ways to sample from any probability distribution. Why would you…

Continue Reading## Behind the Models: Beta, Dirichlet, and GEM Distributions

Behind the Models: Beta, Dirichlet, and GEM DistributionsBuilding Blocks For Non-Parametric Bayesian ModelsTony PistilliBlockedUnblockFollowFollowingMay 31In a future post I want to…

Continue Reading## Teaching Neural Networks to Talk Like Painters Paint

Teaching Neural Networks to Talk Like Painters PaintJesus RodriguezBlockedUnblockFollowFollowingMay 28Conversational interfaces and natural language understanding(NLU) are one of the areas of…

Continue Reading## How to sample from language models

How to sample from language modelsBen MannBlockedUnblockFollowFollowingMay 24Causal language models like GPT-2 are trained to predict the probability of the next…

Continue Reading## The Central Limit Theorem and its Implications

The central limit theorem goes something like this, phrased statistics-encrypted:The sampling distribution of the sample means approaches a normal distribution…

Continue Reading## Hypothesis Testing Glossary for the Weary Reader

Hypothesis Testing Glossary for the Weary ReaderFrom “alpha” to “z-score”Steven RosaBlockedUnblockFollowFollowingJan 26TL;DR — Jump to glossaryWhy So Weary?When I try to read about statistics I…

Continue Reading## Getting started with Visualizations in Python

First things first, don’t even think of relating it with Bar graphs. -Histograms are very different from Bar graphs, in the…

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