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

Continue Reading# probability

## 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## A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning

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

Continue Reading## A Gentle Introduction to Cross-Entropy for Machine Learning

Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information…

Continue Reading## How to Calculate the Divergence Between Probability Distributions

Last Updated on October 18, 2019 It is often desirable to quantify the difference between probability distributions for a given…

Continue Reading## How to Develop a Naive Bayes Classifier from Scratch in Python

Last Updated on October 7, 2019 Classification is a predictive modeling problem that involves assigning a label to a given…

Continue Reading## A Gentle Introduction to Bayes Theorem for Machine Learning

Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can…

Continue Reading## Probability for Machine Learning (7-Day Mini-Course)

This is called the “Boy or Girl Problem” and is one of many common toy problems for practicing probability. Post…

Continue Reading## How to Develop an Intuition for Probability With Worked Examples

Probability calculations are frustratingly unintuitive. Our brains are too eager to take shortcuts and get the wrong answer, instead of…

Continue Reading## How to Develop an Intuition for Joint, Marginal, and Conditional Probability

Probability for a single random variable is straight forward, although it can become complicated when considering two or more variables.…

Continue Reading## A Gentle Introduction to Joint, Marginal, and Conditional Probability

Probability quantifies the uncertainty of the outcomes of a random variable. It is relatively easy to understand and compute the…

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## Discrete Probability Distributions for Machine Learning

The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used…

Continue Reading## A Gentle Introduction to Probability Distributions

Probability can be used for more than calculating the likelihood of one event; it can summarize the likelihood of all…

Continue Reading## What Is Probability?

Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty…

Continue Reading## 5 Reasons to Learn Probability for Machine Learning

Probability is a field of mathematics that quantifies uncertainty. It is undeniably a pillar of the field of machine learning,…

Continue Reading## Resources for Getting Started With Probability in Machine Learning

Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and…

Continue Reading## The information paradox

The information paradoxAndrea BerdondiniBlockedUnblockFollowFollowingJul 9ABSTRACT: The following paradox is based on the consideration that the value of a statistical datum…

Continue Reading## WHAT and WHY of Log Odds

WHAT and WHY of Log OddsPiyush AgarwalBlockedUnblockFollowFollowingJul 8The three main categories of Data Science are Statistics, Machine Learning and Software Engineering.…

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## Demystifying Tensorflow Time Series: Local Linear Trend

Tensorflow time series uses a mean-field variational family for q(z). A mean-field family is a restriction on the relationship among…

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## Summarizing The Great Gatsby using Natural Language Processing

Let’s define the transition probabilities between two sentences as equal to the cosine similarity between the two sentences. We’ll then…

Continue Reading## Converting between nines and sigmas

Nines and sigmas are two ways to measure quality. You’ll hear something has four or five nines of reliability or…

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