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

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## 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,…

Continue Reading## Statistics is the Grammar of Data Science — Part 5/5

Statistics is the Grammar of Data Science — Part 5/5Statistics refresher to kick start your Data Science journeySemi KoenBlockedUnblockFollowFollowingFeb 16This is the 5th (and…

Continue Reading## Solving for probability given entropy

If a coin comes up heads with probability p and tails with probability 1-p, the entropy in the coin flip…

Continue Reading## How to Calibrate Undersampled Model Scores

How to Calibrate Undersampled Model ScoresImbalanced data problems in binary prediction models and a simple but effective way to take care…

Continue Reading## Can you Solve TED’s Frog Riddle? Can TED?

Critics argue that it’s not. For those of you who want to see the problem laid out in detail, you…

Continue Reading## Probability — Fundamentals of Machine Learning (Part 1)

By plugging this into the chain rule, we find that in this scenario we get P(x, y) = P(x|y) ⋅…

Continue Reading## Learning NLP Language Models with Real Data

There are far to many possible sentences in this method that would need to be calculated and we would like…

Continue Reading## Statistics is the Grammar of Data Science — Part 2

To visualise the probability, we plot the dataset as a curve. The area under the curve between two points corresponds…

Continue Reading## Are you mixing up odds with probability?

And in high school they tend to teach us about probabilities, not odds. A probability is defined as the number…

Continue Reading## Markov Chain Monte Carlo in Python

Markov Chain Monte Carlo in PythonWill KoehrsenBlockedUnblockFollowFollowingFeb 9, 2018A Complete Real-World ImplementationThe past few months, I encountered one term again and…

Continue Reading## Marketing Analytics through Markov Chain

Marketing Analytics through Markov ChainRidhima KumarBlockedUnblockFollowFollowingJan 6Image Source : http://setosa. io/ev/markov-chains/Imagine you are a company selling a fast-moving consumer good in the…

Continue Reading## Hyper-parameter Optimization

Hyper-parameter OptimizationJon-Cody SokollBlockedUnblockFollowFollowingJan 3Photo by Paul Green on UnsplashIf you were to count all the possible classification algorithms and their parameters…

Continue Reading## Probability theory and the optimal dating strategy for 2018

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

Continue Reading## Dice, Polls & Dirichlet Multinomials

Even with increasingly better computational tools, such as MCMC, models based on conjugate distributions are advantageous.Beta-BinomialOne of the better known…

Continue Reading## Unfolding Naive Bayes from Scratch: Part 2

When doing the calculations of probability of the given test sentence in the above section, we did nothing but implement…

Continue Reading## Probability Part 2: Conditional Probability

By thinking of conditioning as a restriction on the size of the event space, we can measure the conditional probability…

Continue Reading## Bayes’ Theorem: The Holy Grail of Data Science

1 Statistical resultsThe figure tells us that we have picked…… 148 times a blueberry from the bowl X: n(s=X, y=B)=148……

Continue Reading## Monty Hall’s paradox — solve it by simulation!

D in our case is when the host choosing door B and there is no price behind it.Let’s create a…

Continue Reading## Journey to Understand Bayes’ Theorem Visually

There is also a possibility for another event B to occur after A and the odds of that are denoted…

Continue Reading## Using Markov Chain Monte Carlo method for project estimation

In particular, we are interested in finding the number of story points we can complete in one iteration with 95%…

Continue Reading## Naive Bayes classification from Scratch in Python

All together posterior probability in terms of the joint probability distribution (neglecting denominator P(x)) is written as:Now to calculate each…

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