- Sa. 19 Mai 2018
- MetaAnalysis
- Peter Schuhmacher

#### NumericalStats: What the Metropolis Markov Chain Monte Carlo sampler is good for

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

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HealthPoliticsEconomics | Quant Analytics | Numerics

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- Sa. 19 Mai 2018
- MetaAnalysis
- Peter Schuhmacher

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

- Sa. 12 Mai 2018
- MetaAnalysis
- Peter Schuhmacher

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

- So. 22 April 2018
- MetaAnalysis
- Peter Schuhmacher

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.

- Sa. 14 April 2018
- MetaAnalysis
- Peter Schuhmacher

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.

- So. 01 April 2018
- MetaAnalysis
- Peter Schuhmacher

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.

- So. 25 März 2018
- MetaAnalysis
- Peter Schuhmacher

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.

- Di. 20 März 2018
- MetaAnalysis
- Peter Schuhmacher

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.

- Sa. 03 März 2018
- MetaAnalysis
- Peter Schuhmacher

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.

- Mi. 10 Januar 2018
- MetaAnalysis
- Peter Schuhmacher

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.

- So. 17 Dezember 2017
- MetaAnalysis
- Peter Schuhmacher

We reproduce numerically the meaning of the confidential intervall

- So. 24 September 2017
- MetaAnalysis
- Peter Schuhmacher

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

- Mo. 11 September 2017
- MetaAnalysis
- Peter Schuhmacher

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

- Di. 15 August 2017
- MetaAnalysis
- Peter Schuhmacher

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

- Sa. 12 August 2017
- MetaAnalysis
- Peter Schuhmacher

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.

- So. 06 August 2017
- MetaAnalysis
- Peter Schuhmacher

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

- Mi. 26 April 2017
- MetaAnalysis
- Peter Schuhmacher

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

- Mi. 26 April 2017
- MetaAnalysis
- Peter Schuhmacher

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

- Mi. 08 März 2017
- MetaAnalysis
- Peter Schuhmacher

We use Python to draw some distributions

- Di. 07 März 2017
- MetaAnalysis
- Peter Schuhmacher

We use a simple contingency table to deduce Bayes rule

- Mo. 06 März 2017
- MetaAnalysis
- Peter Schuhmacher

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