Lecture

Applications of Quantum Science: Densities and Statistics

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In course

MICRO-444: La science quantique : une vision singulière

Les étudiants comprennent les caractéristiques de la science quantique. Ils en maitrisent le formalisme et l'appliquent dans les systèmes de base: puits, fils et boites quantiques, qubits. ...

Description

This lecture focuses on the applications of quantum science, specifically discussing densities and statistics. The instructor presents the binomial distribution, explaining the probability of obtaining a certain number of successes in independent trials, where each trial has a specific probability of success. The lecture covers the mean and variance of successes, providing formulas and examples to illustrate these concepts. The discussion then transitions to the Poisson distribution, detailing its derivation from the binomial distribution as the number of trials approaches infinity and the probability of success approaches zero. The instructor explains the mean number of successes in this context and discusses the moments and variance associated with the Poisson distribution. Throughout the lecture, various examples are provided to clarify the application of these statistical concepts in quantum science, emphasizing their relevance in understanding quantum phenomena.

Instructor

Pierre-André Besse

Teaching & PhD Past EPFL PhD Students Klaus Ehrmann Courses La science quantique : une vision singulière MICRO-444 Students understand the characteristics of quantum science. They master the formalism and use it in basic quantum systems: quantum wells, wires, dots or qubits. Physics of semiconductors devices MICRO-312 The students are able to explain the physics of semiconductor devices like diodes, transistors, and MOS devices. They use such devices in fundamental electroni

Official source

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Related concepts (32)

Random variable

A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events. The term 'random variable' can be misleading as it is not actually random nor a variable, but rather it is a function from possible outcomes (e.g., the possible upper sides of a flipped coin such as heads and tails ) in a sample space (e.g., the set ) to a measurable space (e.g., in which 1 corresponding to and −1 corresponding to ), often to the real numbers.

Mean

There are several kinds of mean in mathematics, especially in statistics. Each mean serves to summarize a given group of data, often to better understand the overall value (magnitude and sign) of a given data set. For a data set, the arithmetic mean, also known as "arithmetic average", is a measure of central tendency of a finite set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers x1, x2, ..., xn is typically denoted using an overhead bar, .

Summary statistics

In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations in a measure of location, or central tendency, such as the arithmetic mean a measure of statistical dispersion like the standard mean absolute deviation a measure of the shape of the distribution like skewness or kurtosis if more than one variable is measured, a measure of statistical dependence such as a correlation coefficient A common collection of order statistics used as summary statistics are the five-number summary, sometimes extended to a seven-number summary, and the associated box plot.

Statistical inference

Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population.

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.

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