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We describe basic ideas of the stochastic quantization which was originally proposed by Parisi and Wu. We start from a brief survey of stochastic-dynamical approaches to quantum mechanics, as a historical background, in which one can observe important characteristics of the Parisi-Wu stochastic quantization method that are different from others. Next we give an outline of the stochastic quantization, in which a neutral scalar field is quantized as a simple example. We show that this method enables us to quantize gauge fields without resorting to the conventional gauge-fixing procedure and the Faddeev-Popov trick. Furthermore, we introduce a generalized (kerneled) Langevin equation to extend the mathematical formulation of the stochastic quantization: It is illustrative application is given by a quantization of dynamical systems with bottomless actions. Finally, we develop a general formulation of stochastic quantization within the framework of a (4 + 1)-dimensional field theory.
What else can I help you with?
What are the types of scalar quantization?
There are two types of quantization .They are, 1. Truncation. 2.Round off.
What is the difference between stochastic and random?
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.
What is the difference between Monte Carlo and stochastic methods of simulation?
monte carlo simulation is used to give solutions of deterministic problems whereas stochastic simulation is used for stochastic problems.
What are advantages of vector quantization?
Vector quantization lowers the bit rate of the signal being quantized thus making it more bandwidth efficient than scalar quantization. But this however contributes to it's implementation complexity (computation and storage).
How would you explain to an idiot what 'Stochastic Processes' are?
Buying a lottery ticket daily is deterministic. Winning a lottery and getting a prize is Stochastic.
What is quantization range?
Quantization range refers to the range of values that can be represented by a quantization process. In digital signal processing, quantization is the process of mapping input values to a discrete set of output values. The quantization range determines the precision and accuracy of the quantization process.
When was Stochastic Models created?
Stochastic Models was created in 1985.
What has the author G Adomian written?
G. Adomian has written: 'Stochastic systems' -- subject(s): Stochastic differential equations, Stochastic systems
What is the Different between sampling and quantization?
Sampling Discritizes in time Quantization discritizes in amplitude
What is the difference between uniform quantization and non uniform quantization?
one syllable LOL
What is an ideal quantization error?
The ideal Quantization error is 2^N/Analog Voltage
What are the types of scalar quantization?
There are two types of quantization .They are, 1. Truncation. 2.Round off.
What is the difference between stochastic and random?
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.
What is mid riser quantization?
Mid riser quantization is a type of quantization scheme used in analog-to-digital conversion where the input signal range is divided into equal intervals, with the quantization levels located at the midpoints of these intervals. This approach helps reduce quantization error by evenly distributing the error across the positive and negative parts of the signal range.
What has the author C W Gardiner written?
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
What has the author Quan-Lin Li written?
Quan-Lin Li has written: 'Constructive computation in stochastic models with applications' -- subject(s): Stochastic processes, Stochastic models
What is the relationship between quantisation noise and bandwidth in pcm systems?
Quantization noise is a model of quantization error introduced by quantization in the analog-to-digital conversion(ADC) in telecommunication systems and signal processing.
