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

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## 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…

Continue Reading## 5 Probability Distributions Every Data Scientist Should Know

Here’s your reward. Source: pixabay. Now that you know what a probability distribution is, let’s learn about some of the…

Continue Reading## Probability Distributions Every Data Scientist Should Know

Here’s your reward. Source: pixabay. Now that you know what a probability distribution is, let’s learn about some of the…

Continue Reading## An Introduction to the Powerful Bayes’ Theorem for Data Science Professionals

Source: i. ytimg. com Let E denote the event that a red ball is chosen and A, B, and C…

Continue Reading## Predicting Customer Lifetime Value with “Buy ‘Til You Die” probabilistic models in Python

And above all, how long should we expect a customer to be “alive” for?While these are very common questions among…

Continue Reading## A Gentle Introduction to Maximum Likelihood Estimation and Maximum A Posteriori Estimation

I think some value between 50% and 79% would be more realistic, considering the prior knowledge as well as the…

Continue Reading## A simple Monte-Carlo simulation to solve a Putnam Competition math problem

We’ll take a look at the design and execution of a simple Monte-Carlo simulation using Python. A usually boring train…

Continue Reading## Thompson Sampling For Multi-Armed Bandit Problems (Part 1)

Thompson Sampling For Multi-Armed Bandit Problems (Part 1)Using Bayesian Updating For Online Decision MakingTony PistilliBlockedUnblockFollowFollowingMay 31“Multi-armed bandit” is perhaps the coolest term…

Continue Reading## The Data Science Interview Study Guide

The Data Science Interview Study Guide121 resources to help you land your data science dream jobSeattleDataGuyBlockedUnblockFollowFollowingMay 19Photo by HelloquenceData science interviews, like…

Continue Reading## Can you accurately predict MLB games based on home and away records?

Can you accurately predict MLB games based on home and away records?Take out payroll, batting averages, ERA’s, and any other sabermetric…

Continue Reading## Finding Bayesian Legos

Finding Bayesian LegosHank RoarkBlockedUnblockFollowFollowingMay 14Photo credit: Frédérique Voisin-Demery/Flickr (CC BY 2. 0)Joe, a good family friend, dropped by earlier this week. As…

Continue Reading## Markov Chains and HMMs

Inside AIMarkov Chains and HMMsMain concepts, properties, and applicationsMaël FabienBlockedUnblockFollowFollowingMay 5Hidden MarkovIn this article, we’ll focus on Markov Models, where an…

Continue Reading## Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions

The probabilities corresponding to every pair of values are written P(x = x, y = y) or P(x, y). This…

Continue Reading## 5 Powerful Scikit-Learn Examples

5 Powerful Scikit-Learn ExamplesHerein lies just enough information to make you deadly; 5 models you can learn and apply to…

Continue Reading## How to correctly select a sample from a huge dataset in machine learning

We must perform some kind of high-level comparison with the population made by the other editions. For example, we could…

Continue Reading## Finding the optimal dating strategy for 2019 with probability theory

It’s 1/N. And as n gets larger the larger timeframe we consider, this probability will tend to zero. Alright, you…

Continue Reading## ‘Making big bucks’ with a data-driven sports betting strategy

It surely outperforms random guessing (with equal probability of 1/3 for Win, Draw and Lose), but it does not sound…

Continue Reading## A brief introduction to Markov chains

A brief introduction to Markov chainsDefinitions, properties and PageRank example. Joseph RoccaBlockedUnblockFollowFollowingFeb 24This post was co-written with Baptiste Rocca. IntroductionIn 1998, Lawrence…

Continue Reading## Brief introduction to Markov chains

Brief introduction to Markov chainsDefinitions, properties and PageRank example. Joseph RoccaBlockedUnblockFollowFollowingFeb 24This post was co-written with Baptiste Rocca. IntroductionIn 1998, Lawrence Page,…

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