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# HealthyNumerics

HealthPoliticsEconomics | Quant Analytics | Numerics

# #python 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.

#### The decorative side of mathematical functions

We start with a complex mathematical function and end up with some decorative graphics.

#### An alpine landscape generator

Landscape generation using midpoint displacement in vectorized form

#### 10 pictures Mondrian never has painted

A probalistic art engine for Mondrian style images

#### An accelerated generator of pastel canvas

We try a beautiful art engine and use vectorized forms to accelerate the execution.

#### The Fibonacci sequence in Mondrian style

A short code to plot the Fibonacci sequence as a curve.

#### Convolution and Non-linear Regression

Two algorithms to determine the signal in noisy data

#### Signal generation for distributions and heart beat (ECG wave)

We generate some basic signals and use convolution and windowing to re-construct ECG waves

#### Mapping of mean values and interpolation of distributions

We explore the building of mean values and interpolations when data have more than 1 dimension. We generate numerically a macro finite element so that arbitrary sized data can be anlalysed taking their inherent structure into account.

#### 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

#### 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.

#### Loops over the time

How to create Python loops in the date-time format

#### Time-dependent integration of a one-generation model

We check out which numerical schema is most useful for the temporal integration of one generation of a population.

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

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

#### Computational Fluid Dynamics: An explict Python Schema for the 2dim Diffusion Equation

We introduce a basic schema in computational fluid dynamics for solving the 2dimensional heat equation with a source term and constant diffusivities on an equidistant rectangular grid.

#### Numerical Stats 03: Linear regression by a Monte Carlo method

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

#### Numerical Stats 02: π by Monte Carlo integration

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.

#### Numerical Stats 01: Bootstrapping μ, σ and CI of the mean

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

#### A Python Website & Blog with Pelican and Jupyter

A short guide how to create a Pelican website for blogs with Jupyter