#### Outer Measures

In this post, we will introduce the concept of an outer measure, and we will also illustrate the connection to ...

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#### Aleatory and Epistemic Probabilities

**Introduction**In order to develop a useful theory of events with 'uncertain' outcome, it seems to be reasonable to give ...

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#### Sequences in Metric Spaces

Convergence ca be defined in many different ways. In this post, we study the most popular way to define convergence ...

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#### Fundamentals of Set-Theoretic Topology

Why would one want to generalize notions such as convergence and continuity to a setting even more abstract than metric ...

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#### Nature of Continuity for Measure & Probability Theory – Part I

**Introduction**There are many textbooks, posts, videos and papers about continuity. However, it is hard to find a cohesive introduction ...

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#### Fundamentals of Topology & Metric Spaces

In this brief post, we investigate the topological foundations of analysis. We limit the scope to the topology of a ...

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#### Probability Integral Transform & Quantile Function Theorem

**Introduction**We present simple illustrations, explanations and proofs for two very important theorems,

- the
**probability integral transformation**, and, - the
**quantile ...**

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#### Inner Products, Norms and Metrics

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 Probability & Bayes Rule

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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#### Uncertainty and Capacities in Finance

Monotone Set Functions and the Choquet Integral The quote of the famous statistician George E. P. Box that "all models ...

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#### Decision Problems, Risk and Uncertainty

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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#### Binomial- and Poisson-Mixture Model

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 Distribution

Poisson distributions are very important not only for counting events during a fixed period of time but also for different ...

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#### GPU TensorFlow Installation Guide for Windows

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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#### What is an Empirical Copula?

Introduction Copulas are an important concept in statistics and beyond to describe dependency structures of continuous distributions. However, what can ...

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