Lecture

Risk, Variation, and Uncertainty

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Description

This lecture by the instructor covers the concepts of risk, variation, and uncertainty in the context of disaster risk reduction. It starts with an introduction to risk definition, random experiments, and expected loss estimation. The lecture then delves into the estimation of risk through probability models and loss functions. It emphasizes the importance of distinguishing between variation and uncertainty, highlighting the need to quantify both in applications. The main points stress the representation of data using fitted probability models to estimate probabilities of unseen outcomes and combining them with estimated losses. Despite limitations such as limited data and assumptions in the probability model, the lecture advocates for making the best risk assessments possible.

In MOOC

A Resilient Future: Science and Technology for Disaster Risk Reduction

This course aims to introduce participants to existing and emerging technologies suitable for disaster risk reduction while promoting the overall aim of sustainable development. The course focuses on three main natural hazards- floods, landslides and earthquakes. It also discusses the challenges and limits of adapting and adopting technologies depending on context (Global North and Global South) with examples from Switzerland, Nepal, Colombia, Philippines and other countries.

Official source

Related concepts (23)

Probability

Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin.

Statistical model

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process. When referring specifically to probabilities, the corresponding term is probabilistic model. A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables.

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Bayesian probability

Bayesian probability (ˈbeɪziən or ˈbeɪʒən ) is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses; that is, with propositions whose truth or falsity is unknown.

Probability space

In probability theory, a probability space or a probability triple is a mathematical construct that provides a formal model of a random process or "experiment". For example, one can define a probability space which models the throwing of a die. A probability space consists of three elements: A sample space, , which is the set of all possible outcomes. An event space, which is a set of events, , an event being a set of outcomes in the sample space. A probability function, , which assigns each event in the event space a probability, which is a number between 0 and 1.

Related lectures (64)

Risk and Risk Premium

Explores risk evaluation, equivalent value, and insurance premium in lotteries, as well as discount rate adjustments.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Probability and Statistics: Basics and Applications

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Law of Large Numbers: Strong Convergence