Maxwell's Law, and the Absorption and Emission of Radiation

1926 ◽  
Vol 23 (4) ◽  
pp. 465-471 ◽  
Author(s):  
E. A. Milne
Keyword(s):  
Steady State ◽  
Thermodynamic Equilibrium ◽  
Velocity Component ◽  
Radiant Energy ◽  
Mean Square ◽  
Recoil Momentum ◽  
Famous Paper ◽  
The Mean ◽  
Set Up ◽  
Planck’S Law

In the famous paper in which he developed his theory of radiation, Einstein investigated two problems. He investigated first the problem of what distribution of radiant energy in the steady state would be set up by an enclosed assembly of atoms in thermodynamic equilibrium radiating and absorbing according to the assumptions of his theory; he showed that the distribution so set up was given by Planck's law. He investigated secondly the velocities set up amongst the atoms in consequence of the random variations in direction of the emissions and absorptions; provided that emissions are assumed to be directed, so giving rise to recoil momentum, he showed that the mean square velocity-component in a given direction, , is equal to its equipartition value, given by .

An improved energy envelope stochastic averaging method and its application to a nonlinear oscillator

2016 ◽  
Vol 23 (1) ◽  
pp. 119-130 ◽  
Author(s):  
Yaping Zhao
Keyword(s):  
Steady State ◽  
Probability Density Function ◽  
Probability Density ◽  
Averaging Method ◽  
Stochastic Averaging ◽  
Stochastic Averaging Method ◽  
System Response ◽  
Mean Square ◽  
Fpk Equation ◽  
The Mean

An improved stochastic averaging method of the energy envelope is proposed, whose application sphere is extensive and whose implementation is convenient. An oscillating system with both nonlinear damping and stiffness is taken into account. Its averaged Fokker-Planck-Kolmogorov (FPK) equation in respect of the transition probability density function of the energy envelope is deduced by virtue of the method mentioned above. Under the initial and boundary conditions, the joint probability density function as to the displacement and velocity of the system is worked out in closed form after solving the averaged FPK equation by right of a technique based on the integral transformation. With the aid of the special functions, the transient solutions of the probabilistic characteristics of the system response are further derived analytically, including the probability density functions and the mean square values. A simple approach to generate the ideal white noise is drastically ameliorated in order to produce the stationary wide-band stochastic external excitation for the Monte Carlo simulating investigation of the nonlinear system. Both the theoretical solution and the numerical solution of the probabilistic properties of the system response are obtained, which are extremely coincident with each other. The numerical simulation and the theoretical computation all show that the time factor has a certain influence on the probability characteristics of the response. For example, the probabilistic distribution of the displacement tends to be scattered and the mean square displacement trends toward its steady-state value as time goes by. Of course the transient process to reach the steady-state value will obviously be shorter if the damping of the system is greater.

scholarly journals Statistical-mechanical analysis of adaptive filter with clipping saturation-type nonlinearity

2021 ◽  
Author(s):  
Seiji Miyoshi
Keyword(s):  
Steady State ◽  
Differential Equations ◽  
Asymptotic Analysis ◽  
Adaptive Filter ◽  
Adaptive System ◽  
Adaptive Signal Processing ◽  
Mean Square ◽  
Mean Square Stability ◽  
Statistical Mechanical ◽  
The Mean

Adaptive signal processing is used in broad areas. In most practical adaptive systems, there exists substantial nonlinearity that cannot be neglected. In this paper, we analyze the behaviors of an adaptive system in which the output of the adaptive filter has the clipping saturation-type nonlinearity by a statistical-mechanical method. To represent the macroscopic state of the system, we introduce two macroscopic variables. By considering the limit in which the number of taps of the unknown system and adaptive filter is large, we derive the simultaneous differential equations that describe the system behaviors in the deterministic and closed form. Although the derived simultaneous differential equations cannot be analytically solved, we discuss the dynamical behaviors and steady state of the adaptive system by asymptotic analysis, steady-state analysis, and numerical calculation. As a result, it becomes clear that the saturation value S has the critical value SC at which the mean-square stability of the adaptive system is lost. That is, when S > SC, both the mean-square error (MSE) and mean-square deviation (MSD) converge, i.e., the adaptive system is mean-square stable. On the other hand, when S < SC, the MSD diverges although the MSE converges, i.e., the adaptive system is not mean-square stable. In the latter case, the converged value of the MSE is a quadratic function of S and does not depend on the step size. Finally, SC is exactly derived by asymptotic analysis.<br>

scholarly journals Credibility for the Chain Ladder Reserving Method

2008 ◽  
Vol 38 (02) ◽  
pp. 565-600 ◽  
Author(s):  
Alois Gisler ◽  
Mario V. Wüthrich
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Specific Data ◽  
Conjugate Priors ◽  
Chain Ladder Method ◽  
The Mean ◽  
Chain Ladder ◽  
Set Up ◽  
Classical Chain ◽  
Development Triangle

We consider the chain ladder reserving method in a Bayesian set up, which allows for combining the information from a specific claims development triangle with the information from a collective. That is, for instance, to consider simultaneously own company specific data and industry-wide data to estimate the own company's claims reserves. We derive Bayesian estimators and credibility estimators within this Bayesian framework. We show that the credibility estimators are exact Bayesian in the case of the exponential dispersion family with its natural conjugate priors. Finally, we make the link to the classical chain ladder method and we show that using non-informative priors we arrive at the classical chain ladder forecasts. However, the estimates for the mean square error of prediction differ in our Bayesian set up from the ones found in the literature. Hence, the paper also throws a new light upon the estimator of the mean square error of prediction of the classical chain ladder forecasts and suggests a new estimator in the chain ladder method.

scholarly journals Statistical-mechanical analysis of adaptive filter with clipping saturation-type nonlinearity

2021 ◽  
Author(s):  
Seiji Miyoshi
Keyword(s):  
Steady State ◽  
Differential Equations ◽  
Asymptotic Analysis ◽  
Adaptive Filter ◽  
Adaptive System ◽  
Adaptive Signal Processing ◽  
Mean Square ◽  
Mean Square Stability ◽  
Statistical Mechanical ◽  
The Mean

Adaptive signal processing is used in broad areas. In most practical adaptive systems, there exists substantial nonlinearity that cannot be neglected. In this paper, we analyze the behaviors of an adaptive system in which the output of the adaptive filter has the clipping saturation-type nonlinearity by a statistical-mechanical method. To represent the macroscopic state of the system, we introduce two macroscopic variables. By considering the limit in which the number of taps of the unknown system and adaptive filter is large, we derive the simultaneous differential equations that describe the system behaviors in the deterministic and closed form. Although the derived simultaneous differential equations cannot be analytically solved, we discuss the dynamical behaviors and steady state of the adaptive system by asymptotic analysis, steady-state analysis, and numerical calculation. As a result, it becomes clear that the saturation value S has the critical value SC at which the mean-square stability of the adaptive system is lost. That is, when S > SC, both the mean-square error (MSE) and mean-square deviation (MSD) converge, i.e., the adaptive system is mean-square stable. On the other hand, when S < SC, the MSD diverges although the MSE converges, i.e., the adaptive system is not mean-square stable. In the latter case, the converged value of the MSE is a quadratic function of S and does not depend on the step size. Finally, SC is exactly derived by asymptotic analysis.<br>

On Asymptotic Prediction Problems for Autoregressive Models with Explosive and Other Roots

1989 ◽  
Vol 38 (1-2) ◽  
pp. 43-56 ◽  
Author(s):  
A. K. Basu ◽  
S. Sen Roy
Keyword(s):  
Mean Square Error ◽  
Asymptotic Properties ◽  
Autoregressive Process ◽  
Autoregressive Models ◽  
Mean Square ◽  
The Mean ◽  
Set Up ◽  
Prediction Problems ◽  
Dependent Error

In this paper the asymptotic properties of the estimated predictor of a k-dimensional, pth order autoregressive process with dependent error variables and a general set-up of the roots have been considered. An expression for the mean-square-error of the estimated predictor has also been obtained.

Response Statistics of a Beam-Mass Oscillator Under Combined Harmonic and Random Excitation

1995 ◽  
Vol 1 (2) ◽  
pp. 225-247 ◽  
Author(s):  
Stephen Ekwaro-Osire ◽  
Atila Ertas
Keyword(s):  
Steady State ◽  
Harmonic Component ◽  
Stochastic Averaging ◽  
Random Excitation ◽  
Stochastic Averaging Method ◽  
Time Averaging ◽  
Mean Square ◽  
Mean Square Response ◽  
The Mean ◽  
Non Gaussian

In the present study, the response statistics of a beam-mass oscillator under combined harmonic and random excitation were investigated. The Gaussian and non-Gaussian closure schemes, in conjunction with the stochastic averaging method, were used to solve for the mean square response. The influence of the oscillator parameters on the response statistics was studied. The harmonic component of the excitation was observed to manifest itself, as an oscillation, in the steady-state mean square response. Results obtained showed that the non-Gaussian solution yields higher steady-state mean square responses than those obtained from the Gaussian solution. It was further shown that the harmonic time-varying properties of the oscillator are preserved by omitting the time-averaging in the stochastic averaging procedure.

scholarly journals Credibility for the Chain Ladder Reserving Method

2008 ◽  
Vol 38 (2) ◽  
pp. 565-600 ◽  
Author(s):  
Alois Gisler ◽  
Mario V. Wüthrich
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Specific Data ◽  
Conjugate Priors ◽  
Chain Ladder Method ◽  
The Mean ◽  
Chain Ladder ◽  
Set Up ◽  
Classical Chain ◽  
Development Triangle

We consider the chain ladder reserving method in a Bayesian set up, which allows for combining the information from a specific claims development triangle with the information from a collective. That is, for instance, to consider simultaneously own company specific data and industry-wide data to estimate the own company's claims reserves. We derive Bayesian estimators and credibility estimators within this Bayesian framework. We show that the credibility estimators are exact Bayesian in the case of the exponential dispersion family with its natural conjugate priors. Finally, we make the link to the classical chain ladder method and we show that using non-informative priors we arrive at the classical chain ladder forecasts. However, the estimates for the mean square error of prediction differ in our Bayesian set up from the ones found in the literature. Hence, the paper also throws a new light upon the estimator of the mean square error of prediction of the classical chain ladder forecasts and suggests a new estimator in the chain ladder method.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

The mean-square generalized delayed decision feedback sequence detector

2003 ◽  
Vol 14 (3) ◽  
pp. 265-268 ◽  
Author(s):  
Maurizio Magarini ◽  
Arnaldo Spalvieri ◽  
Guido Tartara
Keyword(s):  
Decision Feedback ◽  
Mean Square ◽  
The Mean

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

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