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Topic: Normalizing constant

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In the News (Wed 21 Aug 19)

  Normalizing constant - Wikipedia, the free encyclopedia
In probability theory, a normalizing constant is a constant by which an everywhere nonnegative function must be multiplied in order that the area under its graph is 1, i.e.
In that context, the normalizing constant is called the partition function.
The constant by which one multiplies a polynomial in order that its value at 1 will be 1 is a normalizing constant.
en.wikipedia.org /wiki/Normalizing_constant   (394 words)

 [No title]
Normalizing education guarantees efficient orientation in the given order of things, perfects competence in its classification and representation, and allows communication and functional behavior, success, security, pleasure, and social progress. It distributes these competences, knowledge, and powers in a socially uneven manner, creating or reproducing social and cultural asymmetries and violences within the system.
Normalizing education achieves this by internalizing in the subject from "outside" the conceptual apparatus, the moral yardsticks and ideals, the consciousness, and the main actual possibilities for reflectivity and social behavior.
The historical fascinating success of normalizing education is offered a grounded explanation by Heidegger’s philosophy: the possibility of unauthentic concern and unauthentic transcendence opens the gate for the human subject to flee from herself, from her responsibility, and from freedom as a danger.
construct.haifa.ac.il /~ilangz/heidegge4.doc   (5743 words)

Normalizing education guarantees efficient orientation in the given order of things, perfects competence in its classification and representation, and allows communication and functional behavior, success, security, pleasure, and social progress.
The normalized subject is swallowed by the meaninglessness of the "Them"; she forgets herself as a finite openness towards infinity, exiled from the possibility of living in the nearness of Being.
The surrendering of the subject to the manifestations of normalizing education is not to be reduced to mere power relations and manipulations as suggested by critical pedagogy or the post-colonial, multicultural, and feminist pedagogies of the day.
construct.haifa.ac.il /~ilangz/heidegger4.html   (6337 words)

 normalizing distributions   (Site not responding. Last check: 2007-10-21)
Posterior Distributions on Normalizing Constants Posterior Distributions on Normalizing Constants This article describes a procedure for defining a posterior distribution on the value of a normalizing constant or ratio of normalizing constants...
This is a short howto on normalizing brains using SPM and skull stripping...
normalizing raindrop size distributions is that it becomes eas-...
www.distribution-software.info-a1.com /distributionsoftware/12/normalizing-distributions.html   (610 words)

 [No title]
In other words, the constant C controls the magnitude of the penalty for pixels which disagree with their neighbors.
The number Z is a normalizing constant which ensures that P(X) is a true probability distribution.
The constant D controls the variance of this Gaussian.
www.cs.cmu.edu /People/ggordon/variational/meanfield   (862 words)

 Normalizing Data Transformations. ERIC Digest.
There are a great variety of possible data transformations, from adding constants to multiplying, squaring, or raising to a power, converting to logarithmic scales, inverting and reflecting, taking the square root of the values, and even applying trigonometric transformations such as sine wave transformations.
The goal of this Digest is to present some of the issues involved in data transformation, with particular focus on the use of data transformation for normalization of variables.
A significant violation of the assumption of normality can seriously increase the chances of the researcher committing either a Type I (overestimation) or Type II (underestimation) error, depending on the nature of the analysis and the non-normality..
www.ericdigests.org /2003-3/data.htm   (1641 words)

 Per-chip Normalizations
The most common way to control for systematic variation is by normalizing to the distribution of all genes.
This sort of normalization assumes that the median signal of the genes on the chip stays relatively constant throughout the experiment.
In such cases, GeneSpring will readjust the background level for your data by adding a constant to all raw control strengths such that the 10th percentile is set equal to 0.
www.fmi.ch /members/edward.oakeley/array/manuals/docs/HelpPages/GSUM-14.html   (816 words)

 valenabs   (Site not responding. Last check: 2007-10-21)
A method is proposed for establishing a posterior distribution on the values of normalizing constants or ratios of normalizing constants estimated from Monte Carlo simulation data.
In the situation in which only dependent draws are available, an approximation to the posterior density on the (ratio of) normalizing constants is presented.
The method also yields a new point estimator of the normalizing constant whose sampling properties compares well to other estimators previously proposed in the statistical literature.
www.stat.unc.edu /abstracts/valenabs.html   (98 words)

For X ~N(0,1), the normalizing constant is 1/sqrt(2 pi) = 0.3989423
i) Estimate the normalizing constant using the exponential (0.1) random variable.
ii) Estimate the normalizing constant using the exponential (2) random variable.
cc.oulu.fi /~hyon/bayes04lab6.html   (322 words)

The problem of estimating normalizing constants arises in a variety of areas of computational statistics, all connected with Monte Carlo integration and the computation of likelihood ratios and posterior distributions.
I offer an alternative formulation in which neither the normalizing constants nor their ratios are computable by calculus or numerical integration.
The new formulation is an infinite-dimensional statistical model in which certain linear functionals of the parameter are the required normalizing constants.
www.crm.umontreal.ca /3cjs/indexan.html   (1107 words)

 Constant Resonance Gain
It turns out it is possible to normalize exactly the resonance gain of the second-order resonator tuned by a single coefficient [
Figure 9.19 shows a family of amplitude responses for the constant resonance-gain two-pole, for various values of
We see an excellent improvement in the regularity of the amplitude response as a function of tuning.
ccrma-www.stanford.edu /~jos/filters/Constant_Resonance_Gain.html   (146 words)

 Normalizing Nonnormal Data - The best data resources online!   (Site not responding. Last check: 2007-10-21)
densities, it is convenient to use the normalizing measure...
among HIV-1C proteins was performed by normalizing the data for the number of study subjects screened...
due to the unknown normalizing constants involved in the...
data.bigfatdirectory.net /index.php?k=normalizing-nonnormal-data   (887 words)

 Normalizing the Distance Metric   (Site not responding. Last check: 2007-10-21)
When this constant is used, I found that the thresholds had to be much smaller than those reported by De Bonet.
The only explanation that I can see is that the normalization constant I used might be off by approximately this factor.
In fact, on the textures that I tried, the normalization constant was of the order of 10^3 to 10^4.
www.cs.toronto.edu /~sallans/csc2522/node7.html   (276 words)

is a set of material properties (bond energies, moduli, diffusion constants, and so forth) which are constant for a given material, but, of course, differ from one material to another.
 must be constants for the members of that group.
 are constants for that group.  So we obtain a second, quite separate, indicator of a good choice of normalizing parameter by examining how nearly constant the quantities
engineering.dartmouth.edu /~defmech/chapter_18.htm   (2112 words)

 Prashant-Small World   (Site not responding. Last check: 2007-10-21)
For a universal constant p>= 1,the node u has a directed edge to every other node within lattice distance p —these are its local contacts.
For universal constant q >=0 and r>= 0, we also construct directed edge from u to q other nodes (the long-range contacts using independent random trials; the i th directed edge from u has endpoint v with probability proportional to [d (u, v)].
Viewing p and q as fixed constant, we obtain a one-parameter family of network model by tuning the value of the exponent r.
bama.ua.edu /~yadav001/smallworld.htm   (1002 words)

 Fast CARs
Sone and Griffith (1995) discussed trade-offs from approximating the difficult normalizing constant (determinant) in maximum likelihood estimation of spatial statistical models.
The sparse matrix technology greatly accelerates computation of the normalizing constant, the major bottleneck in past algorithms.
Normally, even one spatial autoregression of this size could prove challenging.
www.spatial-statistics.com /pace_manuscripts/fastcarscombo_dir/htmlversion/F2CAR3netversion1a.html   (3990 words)

 3.3 Metropolis-Hastings Algorithm
Since the candidate is equal to the current value plus noise, this case is called a random walk M-H chain.
Chib and Greenberg (1994, 1995) discuss a way of formulating proposal densities in the context of time series autoregressive-moving average models that has a bearing on the choice of proposal density for the independence M-H chain.
because the normalizing constant of the full conditional density (the norming constant in the latter expression) cancels in forming the ratio.
www.quantlet.com /mdstat/scripts/csa/html/node27.html   (2001 words)

 Step Size   (Site not responding. Last check: 2007-10-21)
a smaller constant will make the probability of large steps smaller, but not 0.
The constant and m together can be used to manipulate the outcome.
The best combination (for my purpose anyway) seems to be constant = 0,7 and m = 0.3.
www.cs.bath.ac.uk /~ic/stepsize.html   (535 words)

 [No title]   (Site not responding. Last check: 2007-10-21)
They are taken to be known constants Posterior distribution of  EMBED Equation.3  is determined after observing data  EMBED Equation.3  — posterior distribution of  EMBED Equation.3  for given data x.
From now on we denote the posterior of  EMBED Equation.3  by  EMBED Equation.3  Noting that f(x) in (2.1) is a normalizing constant, not depending on EMBED Equation.3 , we express Bayes theorem alternatively  EMBED Equation.3 , (3.3) where proportionality constant is f(x).
Consequently, the proportionality constant is  EMBED Equation.3 .
www.ms.ut.ee /ained/SoomeMCMC/lect2.doc   (1412 words)

 EBsurf program parameters
This is an estimate of the increase in surface temperature based on the constant flux boundary condition solution to the heat transfer equation in one dimension.
This is overestimated by about 25% due to the approximation of a gaussian flux by a constant flux.
(h is a normalizing constant ~1.36, P is beam power, pi=3.1415..., R is beam spot radius, S is beam penetration range, z is depth below surface), which is a good fit to the curve given in Schiller's Electron Beam Technology (1980).
lyre.mit.edu /~powell/Software/outputs.html   (955 words)

 Journal of Computational & Graphical Statistics: Difficulties in estimating the normalizing constant of the posterior ...   (Site not responding. Last check: 2007-10-21)
Difficulties in estimating the normalizing constant of the posterior for a neural network.
Journal of Computational & Graphical Statistics; 3/1/2002; Lee, Herbert K.H. This article reviews a number of methods for estimating normalizing constants in the context of neural network regression.
Model selection or model averaging within the Bayesian approach requires computation of the normalizing constant of the posterior.
www.highbeam.com /library/doc0.asp?DOCID=1G1:84542676&refid=holomed_1   (207 words)

 Journal of Computational & Graphical Statistics: Likelihood estimation and inference for the autologistic model.@ ...   (Site not responding. Last check: 2007-10-21)
Journal of Computational & Graphical Statistics; 3/1/2004; Pettitt, A.N. The autologistic model is commonly used to model spatial binary data on the lattice.
However, if the lattice size is too large, then exact calculation of its normalizing constant poses a major difficulty.
This article presents a method to estimate the normalizing constant in an efficient manner.
www.highbeam.com /library/doc0.asp?DOCID=1G1:114740020&refid=holomed_1   (193 words)

 Citations: Illumination and color in computer generated imagery - Hall (ResearchIndex)   (Site not responding. Last check: 2007-10-21)
This is done by the following computation: where is a matrix chosen to match the particular display device [Hal89] The whole computation has to be done for each combination of and when the BRDF is initially sampled.
The normalizing constant should be chosen such that the resulting BRDF is not brighter or dimmer than before [Hal89] The tristimulus values, still need to be converted to RGB.
These images can be synthesized using the shape of the object (usually represented as surface normals) the reflectance properties of the object s surface, and the distribution of the light sources.
citeseer.ist.psu.edu /context/64554/0   (1803 words)

 Bayes' Rule
The denominator is just a normalizing constant that ensures the posterior adds up to 1; it can be computed by summing up the numerator over all possible values of R, i.e.,
For complicated probabilistic models, computing the normalizing constant P(e) is computationally intractable, either because there are an exponential number of (discrete) values of R to sum over, or because the integral over R cannot be solved in closed form (e.g., if R is a high-dimensional vector).
Graphical models can help because they represent the joint probability distribution as a product of local terms, which can sometimes be exploited computationally (e.g., using dynamic programming or Gibbs sampling).
www.cs.ubc.ca /~murphyk/Bayes/bayesrule.html   (1226 words)

 [No title]   (Site not responding. Last check: 2007-10-21)
N.all <- y.obs:1000 # Compute the normalizing constant.
# The normalizing constant is just the reciprocal of p(y), # the marginal density function of y at y=y.obs.
a <- 1/sum(unnormalized.posterior.density(N.all)) # The Normalized 'Cable car density' function dcable <- function(N) a*unnormalized.posterior.density(N) # Study these statements and try to understand them.
www.stat.columbia.edu /~kerman/Teaching/BDA-exercise-2.10.R   (233 words)

It is much more convenient to normalize all the pixel intensities
is the likelihood of the data given b, and P(D) is a constant which normalizes
where C is a normalizing constant that guarantees that the sum of the probabilities is 1.
hesperia.gsfc.nasa.gov /~schmahl/bayes_mem/bayes_mem.htm   (840 words)

 [No title]
The U function is computed by means C of the backward recursive Miller algorithm applied to the C three term contiguous relation for U(A+K,A,X), K=0,1,...
C This produces accurate ratios and determines U(A+K,A,X), and C hence E(A,X), to within a multiplicative constant C. C Another contiguous relation applied to C*U(A,A,X) and C C*U(A+1,A,X) gets C*U(A+1,A+1,X), a quantity proportional to C E(A+1,X).
The normalizing constant C is obtained from the C two term recursion relation above with K=A. C The maximum number of significant digits obtainable C is the smaller of 14 and the number of digits carried in C double precision arithmetic.
www.ualberta.ca /CNS/RESEARCH/Software/NumericalSF/dexint.f   (560 words)

 Estimating Normalizing Constants and Reweighting Mixtures in Markov Chain Monte Carlo (ResearchIndex)   (Site not responding. Last check: 2007-10-21)
Estimating Normalizing Constants and Reweighting Mixtures in Markov Chain Monte Carlo (ResearchIndex)
Estimating Normalizing Constants and Reweighting Mixtures in Markov Chain Monte Carlo
Abstract: Markov chain Monte Carlo (the Metropolis-Hastings algorithm and the Gibbs sampler) is a general multivariate simulation method that permits sampling from any stochastic process whose density is known up to a constant of proportionality.
citeseer.ist.psu.edu /35769.html   (229 words)

We consider Markov processes of DNA sequence evolution in which the instantaneous rates of substitution at a site are allowed to depend upon the states at the sites in a neighbourhood of the site at the instant of the substitution.
We characterize the class of Markov process models of DNA sequence evolution for which the stationary distribution is a Gibbs measure, and give a procedure for calculating the normalizing constant of the measure.
We develop an MCMC method for estimating the transition probability between sequences under models of this type.
www.math.ku.dk /cal/events/648.htm   (152 words)

 A Dependent-Rates Model and an MCMC-Based Methodology for the Maximum-Likelihood Analysis of Sequences with Overlapping ...
a site was assumed to be constant over time.
The normalizing constant Z is found by summing up the
and the normalizing constant in equation (A.4) is
mbe.oxfordjournals.org /cgi/content/full/18/5/763   (6356 words)

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