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# HealthyNumerics

HealthPoliticsEconomics | Quant Analytics | Numerics

# #statistics Articles

#### MachineLearning: A simple but complete artificial Neural Network

Artificial Neural Networks "learn" to perform tasks by considering examples. They do this without any prior knowledge. Instead, they automatically generate identifying characteristics from the examples that they process. Beside the success of an AI model the confusion or error matrix is an important tool. It gives a risk profile we have to deal with when using AI models.

#### MachineLearning: The Confusion Matrix in the AI Communitiy

How good is good enough? How wrong is still an acceptable level? The confusion or error matrix is an important tool. It gives a risk profile we have to deal with when using AI models. It's exactly the same tool that is often used for the evaluation of medical interventions.

#### MachineLearning: Gradient descent from Nobel Laureates to chocolate consumption

Evaluating the appropriate parameters of a model is the core of every machine learning algorithm. In neural networks such a procedure has to be repeated over an over. Beceause of the non-linearities numerical approaches which approximate the solution iteratively are an important class of solution.

#### MachineLearning: How to analyze longitudinal studies with time series clustering

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.

#### 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.

#### How to climb the Matterhorn with a Markov Chain Monte Carlo walk

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

#### Computational Fluid Dynamics: Markov Chain Monte Carlo in 2 dimensions

Stochastic modeling is a commonly used methodology in health economics and outcomes research (HEOR). We provide a visible insight in Markov Chain Monte Carlo modelling by a health related topic, the distribution of pollutants.

#### NumericalStats: WHY to switch to distribution vectors

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.

#### NumericalStats: WHY to switch from a decision tree to a Markov model

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.

#### NumericalStats: How to randomly sample your empirical arbitrary distribution

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.

#### BasicStats: The secret of the CI

We reproduce numerically the meaning of the confidential intervall

#### Meta Analysis 01: Clinical multi center study

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

#### Basic Stats 32: Multiple linear Regression, ANOVA

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

#### Basic Stats 31: Linear Regression

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

#### Basic Stats 02: A mechanical approach to the Bayesian rule

We use a simple contingency table to deduce Bayes rule

#### Basic Stats 01: Rules of probability and bridge to the Bayesian formula

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