Energy & Green Tech

Using machine learning to find reliable and low-cost solar cells

Researchers at the University of California, Davis College of Engineering are using machine learning to identify new materials for high-efficiency solar cells. Using high-throughput experiments and machine learning-based ...

Machine learning & AI

Portrait of a Google AI art project as a poetic you

Roses are red violets are blue, AI writing poems? Can't be true. Or can it? And if so, how low can we go in expectations? Brush low expectations aside for now, as Google is on to something special, and that is, AI for self-portrait ...

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 ...

Computer Sciences

Predicting British railway delays using artificial intelligence

Over the past 20 years, the number of passengers traveling on British train networks has almost doubled to 1.7 billion annually. With numbers like that it's clear how much people rely on rail service in Great Britain, and ...

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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