stochastic processes

He is best known for his work on stochastic processes.

It is a key process in terms of which more complicated stochastic processes can be described.

Models for time series data can have many forms and represent different stochastic processes.

It is common in stochastic processes to care about the expected value of a variable given a previous information set.

At the same time he was considering questions unrelated to stochastic processes.

The most common way to create compositions through mathematics is stochastic processes.

Queue theory is based on markov chains and stochastic processes.

In the 1990s, Gallager's interests shifted back to information theory and to stochastic processes.

Analysis of stochastic processes reveals that they present themselves as broad peaks on a power spectrum.

Brownian excursion, a concept in the theory of stochastic processes.

Many texts on stochastic processes do, indeed, assume a probability space but never state explicitly what it is.

Large areas have large populations, which are less likely to go extinct through stochastic processes.

Thus, Bachelier is considered a pioneer in the study of financial mathematics and stochastic processes.

In the 1930s and 1940s, the mathematical foundations for stochastic processes were developed .

As such, he established the relation of Kolmogorov's equations with stochastic processes.

In the mathematics of probability, a subordinator is a concept related to stochastic processes.

His monograph Stochastic processes in physics and chemistry (1981) is considered a classic.

IE specialty courses that follow these include quality control, simulation, and stochastic processes.

We may also define functions on discontinuous stochastic processes.

His work often involves computer-aided algorithmic composition and stochastic processes.

One of the simplest continuous-time stochastic processes is Brownian motion.

Firstly, we are now dealing with random variables (more precisely, stochastic processes).

The distribution is now used in the fields of stochastic processes and of biomathematics.

Stochastic processes can be classified according to the cardinality of its index set (usually interpreted as time) and state space.

Generalising, this theory is applied to various stochastic processes concerned with geometric and incidence questions.