“The odds are good for my favourite team.”, might somebody say but what do they mean with odds? Odds We will talk today about odds. Not just because to understand the language of sports betters but also because they are quite important for Bayesian updating. Odds can reduce our calculations and therefore our computation if … Continue reading Bayesian Inference IV – Odds

# Bayesian Inference III – Probabilistic Predictions

We are sometimes more interested in the probability that an outcome occurs than in the probability of the hypotheses. We then talk about probabilistic predictions. Probabilistic Predictions Probabilistic predictions are when we talk about things like: “It will rain tomorrow with a probability of 60%, and not rain with a probability of 40%” We often use … Continue reading Bayesian Inference III – Probabilistic Predictions

# Experiment – Monty Hall Problem

After I'd covered the Monty Hall Problem in the last blog-post, I wrote an experiment in python, which shows that when the number of doors increases but the rules of the game stay the say, our probability of winning given that we switched converges to 1. The probability of winning given we stayed converges to … Continue reading Experiment – Monty Hall Problem

# Monty Hall Problem

The Monty Hall Problem is a great example to demonstrate Bayesian Updating with Discrete Priors. Monty Hall Problem The monty hall problem is based on a TV show in which one could either win a car or nothing. The participant was presented three doors. Behind one was a car, behind the others were goats. After … Continue reading Monty Hall Problem

# Bayesian Inference II – Bayesian Updating Discrete Priors

We get closer and closer to the exciting, interesting parts of data science. Bayesian Inference or more precisely Bayesian updating is one part of that. It is used in some machine learning algorithms and allows us to update probabilities when we get new data. Bayesian Updating Discrete Priors We will today just look at discrete … Continue reading Bayesian Inference II – Bayesian Updating Discrete Priors

# Bayesian Inference I – Maximum Likelihood Estimate

There are times when we don't know the values of parameters. The maximum likelihood estimate and following methods will enable us the estimate the values of parameters. Maximum Likelihood Estimate (MLE) What is the maximum likelihood estimate? The maximum likelihood estimate gives us the biggest probability for an experiment it tells us therefore for which … Continue reading Bayesian Inference I – Maximum Likelihood Estimate

# Introduction XII -Order Statistics

We've used order statistics already when we covered auction theory but some of you might not have known what order statistics are. I hope I can enlighten those today. Order Statistics What are Order Statistics? Order statistics are what the name says. They are the probability that a given value of i.i.d random variables is the nth … Continue reading Introduction XII -Order Statistics