search for: estimation

...mated cost of 0 for instruction: br label %11 Cost Model: Found an estimated cost of 1 for instruction: %12 = load i32* %1 Cost Model: Found an estimated cost of 0 for instruction: ret i32 %12 Is it expected to only have 1s and 0s output? Also, what target architecture is being used for the estimations? (Is it auto detecting the architecture I'm on?) Thanks, Steve -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20150805/ebffcedc/attachment-0001.html>

On Wed, Jan 15, 2014 at 5:30 PM, Nadav Rotem <nrotem at apple.com> wrote: > Was the vectorizer successful in unrolling the loop in quantum_sigma_x? I > wonder if 'size’ is typically high or low. No. The vectorizer stated that it wasn't going to bother with the loop because it wasn't profitable. Specifically: LV: Checking a loop in "quantum_sigma_x" LV: Found a

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

Thanks to the help of many on this list, I am now an R user and have been able to write some functioning code to do matching estimation. I have two for loops (i in 1:3, and j in 0:2). Within the loops, I had been creating matrices of relevant estimation coefficents in order to make lots of LaTeX tables. Well, now I want to be able to combine the results of many different estimations from within the loops into one larger table ou...

....frame(cbind(n,y)) colnames(z) <- c("n","y") library(fExtremes) z <- split(z, z$n) res2 <-lapply(z, function(x){ ?????????????? m <- as.numeric(x$y) ?????????????? gevFit(m, block = 1, type = c("pwm")) ??????????????? }) > res2 $`1` Title: ?GEV Parameter Estimation Call: ?gevFit(x = m, block = 1, type = c("pwm")) Estimation Type: ? gev pwm Estimated Parameters: ?????? xi??????? mu????? beta 0.1033614 2.5389580 0.9092611 Description ? Wed Jun 29 23:07:48 2011 $`2` Title: ?GEV Parameter Estimation Call: ?gevFit(x = m, block = 1, type = c("...

Dear R-list, I have made a simple kernel density estimation by x <- c(2,1,3,2,3,0,4,5,10,11,12,11,10) kde <- density(x,n=100) Now I would like to know the estimated probability that a new observation falls into the interval 0<x<3. How can I integrate over the corresponding interval? In several R-packages for kernel density estimation I did no...

Dear R Users, Here is another probelm/question. I would like to run some panel regressions with R. Therefore I have combined several time periods of data for different individuals in my database. I have already run pooled OLS but I would need to calculate a Fixed Effects Estimator (within estimator). Unfortunately I couldn't find anything like that in the RSearch and I suppose that lme

This option prints the estimated size of the data that will be copied from the source disk. For interest, the test prints: 3747840 ../test-data/phony-guests/windows.img Estimate: 3710976 --- v2v/Makefile.am | 2 ++ v2v/cmdline.ml | 17 ++++++++++-- v2v/cmdline.mli | 2 ++ v2v/test-v2v-print-estimate.sh | 47 +++++++++++++++++++++++++++++++++

Hello, I have a loop with 1000 repetitions which includes OR computation of an exposure factor and outcome. I compute OR like this: t<-table(exposure,outcome) oddsratio(t)$measure["estimate"] This gives me the estimates for exposure=0 and exposure=1 but exposure=0 is the reference group and i need only the estimate for exposure=1. I specified a matrix OR with 3 columns (for OR

Is it possible to simultaneously estimate mean and GARCH parameters in R? In other words, I would like to estimate the normal regression equation Y = b X + u and simultaneously do a garch process on the u's to correct the standard errors. I was thinking maybe something with systemfit(), but I can't quite come up with it. Thanks, Tobias --

...outcome variable over time. Summary.rq's robust variance estimator is not applicable to repeated-measures data. I question whether the block (cluster) bootstrap variance estimator, which can accommodate intraclass correlation, would perform well. Can anyone suggest alternatives for variance estimation in this situation? Regards, Jim James W. Shaw, Ph.D., Pharm.D., M.P.H. Assistant Professor Department of Pharmacy Administration College of Pharmacy University of Illinois at Chicago 833 South Wood Street, M/C 871, Room 266 Chicago, IL 60612 Tel.: 312-355-5666 Fax: 312-996-0868 Mobile Tel.: 215...

Hi, I have experimented with enabling the LoopVectorizer for SystemZ. I have come across a loop which, when vectorized, seems to have been poorly generated. In short, there seems to be a completely unnecessary sequence of shufflevector instructions, that doesn't get optimized away anywhere. In other words, there is a shuffling so that leads back to the original vector: [0 1 2 3

...;m trying to estimate - should I give those parameters initial values?). I've looked into the other functions to get least squares estimates (e.g. lm() ) but I'm not sure how to use that for a system of equations. I have some experience with R but I'm a novice when it comes to parameter estimation, so any help would be much appreciated! Thank you! -- View this message in context: http://r.789695.n4.nabble.com/Estimating-model-parameters-for-system-of-equations-tp4073490p4073490.html Sent from the R help mailing list archive at Nabble.com.

Dear All, we (Ott Toomet and I) would like to add functions for maximum likelihood (ML) estimations of generalized tobit models of type 2 and type 5 (*see below) in my R package for microeconomic analysis "micEcon". So far we have called these functions "tobit2( )" and "tobit5( )". Are these classifications well known? How are these functions called in other so...

Hi, I would like estimate a model for function of production's Coob-Douglas using maximum likelihood. The model is log(Y)= beta[1]+beta[2]*log(L)+beta[3]*log(K). I tried estimate this model using the tools nlm ( ) and optim ( ) using the log-likelihood function below: > mloglik <- function (beta, Y, L, K) { + n <- length(Y) + sum ( (log(Y)-

On Tue, Aug 14, 2018 at 8:29 PM Richard W.M. Jones <rjones@redhat.com> wrote: > This option prints the estimated size of the data that will be copied > from the source disk. > > For interest, the test prints: > > 3747840 ../test-data/phony-guests/windows.img > Estimate: 3710976 > Why not use qemu-img measure on the overlay? It gives a conservative estimate that

...odel with two parameters (there is another with three parameters): the mean and the standard deviation. When you fit that model -the lognormal distribution- to a sample, you are estimating these two parameters. If you maximise the likelihood for your data as a function of the two parameters the estimation process, if successful, will produce the two estimates and the corresponding standard errors of those estimates (plus the estimated covariance between the estimates). Both parameters, the lognormal mean and the lognormal standard deviation, are unknown and are estimated so that each one has its...

This option prints the estimated size of the data that will be copied from the source disk. Currently this overestimates by the size of the qcow2 header, but for real disk images that doesn't matter much. For example: $ virt-builder fedora-27 $ virt-v2v -i disk fedora-27.img -o null --machine-readable --print-estimate [...] virt-v2v: This guest has virtio drivers installed. [ 44.0] Mapping

"help" We want to estimate the number of caribou in Jasper. We recently conducted an aerial survey and saw 70 uncollared caribou and 8 of 11 collared caribou. We want to estimate the number of caribou in this population with 95% confidence limits. Gary White uses the hypergeometric distribution and determines the population estimates using maximum likelihood and 95%CL as

Hi, I am taking a survival class. Recently I need to do the Nelson-Aalen estimtor in R. I searched through the R help manual and internet, but could not find such a R function. I tried another way by calculating the Kaplan-Meier estimator and take -log(S). However, the function only provides the summary of KM estimator but no estimated values. Could you please help me with this? I would