Computer Sciences

The privacy risks of compiling mobility data

A new study by MIT researchers finds that the growing practice of compiling massive, anonymized datasets about people's movement patterns is a double-edged sword: While it can provide deep insights into human behavior for ...


New model better predicts our daily travel choices

An EPFL engineer has developed a forecasting model that factors in not just our commuting habits, but also our activities during the day. Her flexible approach incorporates the idea of trade-offs in order to deliver more ...

Machine learning & AI

A statistical model for ensuring children's safe and sound mobility

A research team led by Kojiro Matsuo, an associate professor at the Department of Architecture and Civil Engineering within the Toyohashi University of Technology, and Kosuke Miyazaki, a professor at the Department of Civil ...

Computer Sciences

Chess: How to spot a potential cheat

A few years ago, the chess website temporarily banned U.S. grandmaster Hans Niemann for playing chess moves online that the site suspected had been suggested to him by a computer program. It had reportedly previously ...

Computer Sciences

A better statistical model for environmental data

By clarifying inconsistencies in published theories and devising a flexible statistical model, KAUST researchers have established a more informed and reliable basis for selecting the most suitable statistical model for environmental ...

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Statistical model

A statistical model is a set of mathematical equations which describe the behavior of an object of study in terms of random variables and their associated probability distributions. If the model has only one equation it is called a single-equation model, whereas if it has more than one equation, it is known as a multiple-equation model.

In mathematical terms, a statistical model is frequently thought of as a pair (Y,P) where Y is the set of possible observations and P the set of possible probability distributions on Y. It is assumed that there is a distinct element of P which generates the observed data. Statistical inference enables us to make statements about which element(s) of this set are likely to be the true one.

Three notions are sufficient to describe all statistical models.

One of the most basic models is the simple linear regression model which assumes a relationship between two random variables Y and X. For instance, one may want to linearly explain child mortality in a given country by its GDP. This is a statistical model because the relationship need not to be perfect and the model includes a disturbance term which accounts for other effects on child mortality other than GDP.

As a second example, Bayes theorem in its raw form may be intractable, but assuming a general model H allows it to become

which may be easier. Models can also be compared using measures such as Bayes factors or mean square error.

This text uses material from Wikipedia, licensed under CC BY-SA