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.
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.
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.
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
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.
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.
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.