Stochastic effects refer to health effects that occur by chance and are characterized by a probability of occurrence rather than a threshold level of exposure. These effects are typically associated with low doses of radiation or chemicals, where the likelihood of an adverse outcome, such as cancer or genetic mutations, increases with the dose but is not guaranteed. Unlike deterministic effects, which have a clear cause-and-effect relationship and a threshold level, stochastic effects can manifest long after exposure and vary among individuals.
The definition to the term "Stochastic Process" is: A statistical process involving a number of random variables depending on a number variable. Which in most cases, is time.
Stochastic means non-deterministic. This means that something contains an inherent degree of randomness. For more detail, you should consult a dictionary or more detailed literature on probability theory.
Mathematical model is exact in nature.it has Beta zero and Beta one and no stochastic or disturbance variables. Econometric model represents omitted variable, error in measurement and stochastic variables.
A Stochastic error term is a term that is added to a regression equation to introduce all of the variation in Y that cannot be explained by the included Xs. It is, in effect, a symbol of the econometrician's ignorance or inability to model all the movements of the dependent variable.
A stochastic event is a random occurrence that is subject to chance and uncertainty, often modeled using probability theory. Unlike deterministic events, which have predictable outcomes, stochastic events can yield different results even under identical conditions. Examples include rolling a die, stock market fluctuations, and weather changes. These events are often analyzed in fields like finance, statistics, and science to understand and manage risk.
No threshold
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Stochastic Models was created in 1985.
G. Adomian has written: 'Stochastic systems' -- subject(s): Stochastic differential equations, Stochastic systems
Wikipedia states that stochastic means random. But there are differences depending on the context. Stochastic is used as an adjective, as in stochastic process, stochastic model, or stochastic simulation, with the meaning that phenomena as analyzed has an element of uncertainty or chance (random element). If a system is not stochastic, it is deterministic. I may consider a phenomena is a random process and analyze it using a stochastic simulation model. When we generate numbers using a probability distribution, these are called random numbers, or pseudo random numbers. They can also be called random deviates. See related links.
C. W. Gardiner has written: 'Handbook of Stochastic Methods' 'Stochastic methods' -- subject(s): Stochastic processes 'Quantum noise' -- subject(s): Stochastic processes, Quantum optics, Josephson junctions
Quan-Lin Li has written: 'Constructive computation in stochastic models with applications' -- subject(s): Stochastic processes, Stochastic models
A stochastic disturbance term is a random variable included in a statistical model to account for unexplained variability or uncertainty in the data. It represents the effects of unobserved factors that are not explicitly modeled but can influence the outcome of an analysis. By incorporating this term, the model can better capture the randomness or unpredictability in the data.
monte carlo simulation is used to give solutions of deterministic problems whereas stochastic simulation is used for stochastic problems.
You can download free copy of Stochastic Filtering by Ramaprasad Bhar here. /forexebooks.co.in/stochastic-filtering-applications-finance-ebook/
Hiroaki Morimoto has written: 'Stochastic control and mathematical modeling' -- subject(s): Stochastic control theory, Optimal stopping (Mathematical statistics), Stochastic differential equations
The mathematical theory of stochastic integrals, i.e. integrals where the integrator function is over the path of a stochastic, or random, process. Brownian motion is the classical example of a stochastic process. It is widely used to model the prices of financial assets and is at the basis of Black and Scholes' theory of option pricing.