## Data analysis a Bayesian tutorial

Book review Data analysis a Bayesian tutorial The second edition of this book is written by Devinder Sivia (chapters 1 through 8) with John Skilling (chapters 9 and 10). The book gives a concise (in 250 pages), but full description of Bayesian data analysis. I know...

read more## PRML

Boek review Pattern Recognition and Machine Learning Pattern Recognition and Machine Learning is often the first book I consult when I need to know something about a data subject. It is written by Christopher Bishop in a clear, smooth style. It is completely...

read more## How smart am I?

What is my IQ? I once participated in an IQ test and scored 120 points. As a data scientist I wonder: What is my real IQ? Because this is only one test. According to Wikipedia, the accuracy of the IQ test is about 3 points. So my real IQ will be somewhere...

read more## the Bayesian approach

Use everything you know You cannot do inference without making assumptions - Sir David MacKay (1967 – 2016) In What is your Model the following question was asked: How much do you earn tomorrow if you have earned €200 today? And yesterday €100, €200 the day before and...

read more## What is your model?

The best fit is not the best model How much do you expect to earn tomorrow if you have earned today € 200? And yesterday € 100, the day before € 200, and three days ago € 100? These are your data and they are shown as the four orange dots. To predict you...

read more## Matching demand and supply

An application of Simulated Annealing My wife has a perennial problem. Working at a university every year she has to assign some 200 students to 20 thesis supervisors. With Excel as her only tool and many matchings, an unthankful task. Time for...

read more## Waiting for Uber

In Amsterdam, on average, you are picked up within 3.5 minutes. According to Uber, though it adds the disclaimer The pick-up time may vary due to traffic or other circumstances. Now, how long in advance should you book your ride? You could...

read more## The inevitable uncertainty

Uncertainty is everywhere. A key concept in data analysis is that of uncertainty. It arises both through the noise in the measurements, as well as through the finite size of data sets. Probability theory provides a consistent framework to...

read more## Gauss hides another secret

The amazing Gauss. The familiar Gaussian distribution turns out to be the unique solution for a another important problem. Suppose, you only know the first moment (mean) and the second moment (noise power)? What is "the best" probability...

read more## Information and Data

Data is not Information. This may come as a surprise, but it is true. Data are nothing more than the correct administration of measurements or observations, and nothing less. The Data are set of values, or numbers if you prefer. Information is...

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