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In this post, we will introduce the concept of an outer measure, and we will also illustrate the connection to ...
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Introduction In order to develop a useful theory of events with 'uncertain' outcome, it seems to be reasonable to give ...
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Convergence ca be defined in many different ways. In this post, we study the most popular way to define convergence ...
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Why would one want to generalize notions such as convergence and continuity to a setting even more abstract than metric ...
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Introduction There are many textbooks, posts, videos and papers about continuity. However, it is hard to find a cohesive introduction ...
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In this brief post, we investigate the topological foundations of analysis. We limit the scope to the topology of a ...
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Introduction We present simple illustrations, explanations and proofs for two very important theorems,

• the probability integral transformation, and,
• the quantile ...
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Most people do have an intuitive understanding of the real number system: it is the number system that should be ...
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Conditional Probabilities Let us consider a probability measure $P: \mathcal{A} \rightarrow \mathbb{R}$ of a measurable space $(\Omega, \mathcal{A})$. Further, let ...
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Monotone Set Functions and the Choquet Integral The quote of the famous statistician George E. P. Box that "all models ...
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In this post, an introduction to decision-making under risk and uncertainty is provided. To this end, basic concepts and components ...
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Introduction The assumption of independent and identically distributed random variables, short i.i.d., my be quite handy since it simplifies several ...
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Poisson distributions are very important not only for counting events during a fixed period of time but also for different ...
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Before we actually start the installation process of the GPU-accelerated Python API of TensorFlow on a Windows platform, we shortly ...
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Introduction Copulas are an important concept in statistics and beyond to describe dependency structures of continuous distributions. However, what can ...
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