With the increasing possibilities to gather longitudinal data, there is an interest in mining profiles in form of time series data. The key question is how to figure out and to group similarities and dissimilarities between the profiles.

We use the Metropolis-Hasting algorithm to sample a 2-dimensional empirical distribution.

We let your robot climb the Matterhorn with a Markov Chain Monte Carlo walk

The Monty Hall problem came out of a US TV-game in the 1970's and got wide publicity about problem solving statistically. It can be transfered easely to medical research how to seek a strategy that proofs to be the most suitable to reach ceratin aims in healthcare.

Using the coin flipping example, we give some arguments WHY the use of distribution vectors can be helpful as a preparation for Monte Carlo Markov Chain models and others and how this changes the role of medical researchers.

We give some arguments, why a change from a decision tree to a Markov model could be motivated. We provide a code of 7 lines to run a Markov model.

The output of a dignostic test is often not binary but continuous. The transformation of the continuous output into a binary variable influences the outcome of the test.

Given a diagnostic test with sensitivity 0.90 and specifity 0.95. Then, with a prevalence of 0.02, in medical practice 27% of the positive tests will be right and 73% of the positive tests will be wrong.

We provide a simple sampling engine which allows to generate random numbers that are distributed as an empirical and arbitrary distribution given as a data array.

We reproduce numerically the meaning of the confidential intervall

A first glance at the numerical analysis of decision trees for medical decision making

We simulate a multi center study of a diastolic blood pressure decreasing drug.

By performing linear regression by a Monte Carlo method we get an estimate (mean, standard deviation, standar error) of the slope and the intercept.

We use a Monte Carlo method with a code of 6 lines for the integration of mathmatical functions. In the case of a circle we can determin π.

As a first example for numerical statistics we introduce bootstrapping which belongs to the class of Monte Carlo methods.

Elementary methods for data flow and statistics with Python, Pandas, NumPy, StatsModels, Seaborn, Matplotlib

Elementary methods for data flow and statistics with Python, Pandas, NumPy, StatsModels, Seaborn, Matplotlib

We use Python to draw some distributions

We use a simple contingency table to deduce Bayes rule

By summarizing the rules of probability we build a bridge to the Bayesian formula