I'm a Munich based mathematician.
My work interest is in quantitative modelling of natural and social phenomena, from topics in finance to information retrieval and artificial neural networks.
I respond to .
Here I do a little experiment showing that all investigated
alternative search engines are infact search proxies and fully
dependent on MS Bing search results. Moreover Qwant and Brave Search
mislead users by stating that they alledgely operate
their own index and technology -- which is in contrast to empirical data.
There has been a lot interest on simulation the devolpment of infectious deseases since the COVID-19
pandemic of 2019/20. Many different models and approaches exist to computationally simulate the
spread of infectious deseases.
Using published results for the performance of state-of-the-art AI
document-vector embeddings and semantic hashing I evaluated a canonical
document-vector retrieval system boosted by approximate nearest neighbour
The cosine measure is the prevailing similarity function for the document vector model of IR. We discuss a its
connection to the intrinsic dimension.
Hierarchical agglomerative clustering (HAC) is a family of different
algorithms to perform grouping of data. HAC starts by merging the two
data points with smallest distance into a new cluster and finishes with
one big cluster describing the data.
Initialization of k-means can have a big impact on the performance of the
algorithm. Straight forward random initialization can lead to many more
iterations compared to a better initialization using kmeans++.
Here I show how to price a simple GMDB (unit linked
insurance product) using installment options
and calculate the premium using a binominal tree approach. The
analysis shows that non-rational policy holder behaviour leads
to strong mispricing.