Sentences with phrase «known model bias»

Not exact matches

-- Still, same story persists about earnings growth and economic backdrop (problem is that no one knows how to model in impact of tariffs)- many are still afflicted with confirmation bias and narrow vision

«Though we know many of the kinds of biases that can occur, modeling them has to occur on a sample - by - sample basis,» he added.

This step in and of itself biases the model to work successfully in cross validation although an argument could be made that this is acceptable because the gene list is already known to be relevant.

The most sophisticated approach uses a statistical technique known as a value - added model, which attempts to filter out sources of bias in the test - score growth so as to arrive at an estimate of how much each teacher contributed to student learning.

The SPP is largely (40 %) based on school - level value - added model (VAM) output derived via the state's Pennsylvania Education Value - Added Assessment System (PVAAS), also generally known as the Education Value - Added Assessment System (EVAAS), also generally known as the VAM that is «expressly designed to control for out - of - school [biasing] factors.»

Sample members taking the survey would sometimes leave answers blank or respond that they did not know.This issue would be okay if we knew the missing values were random, however most often there is systematic reasons for why some people leave answers blank thus presents bias into the model.

The Sport's spec includes double - wishbone suspension front and rear, 20 - inch wheels with road - biased tyres, active anti-roll technology, known as Dynamic Response (standard on supercharged models, optional on others), air suspension, a quicker steering ratio than before (also with a variable ratio) and four - piston Brembo front brakes to complete the dynamic package.

When it comes to model performance, there is a concept known as «bias correction» that was debated.

We need to determine parameters that are not well known, deal with inadequately modeled physics, and address significant biases in the forcing fields.

In this case, there has been an identification of a host of small issues (and, in truth, there are always small issues in any complex field) that have involved the fidelity of the observations (the spatial coverage, the corrections for known biases), the fidelity of the models (issues with the forcings, examinations of the variability in ocean vertical transports etc.), and the coherence of the model - data comparisons.

The question about uncertainty is a question about information about processes, whether understood, random variations (known as «noise» or stochastic processes), or systematic model shortcomings (biases).

If a model runs with a known bias, what is the value of that model?

This WP aims to improve the resolution of ocean models, as well as the representation of physical and biogeochemical processes, in key regions of the world's oceans (particularly tropical coastal regions, the Southern Ocean and high Northern latitudes) to reduce well known biases in ESMs.

That's the eternal problem with model - based solutions — it's VERY hard to avoid bias in the models, no matter what side you're on, since if you get a surprising result, it's very easy and very tempting to assume the model is wrong, not your expectations.

I would have liked to see mention of uncertainty that inherent in examining short term data, whether the end points used introduces an element of bias, whether the «pause» is on a much higher plateau of warming than in the past, whether decadel cycles in ocean heat displacement may have interacted with the the known minimum levels of solar activity (not modelled) to cause this «pause».

[17] When the models within a multi-model ensemble are adjusted for their various biases, this process is known as «superensemble forecasting».

The raw daily climate model simulation results were bias corrected according to the ISI - MIP protocol (1, 23), despite known caveats with respect to the use of bias correction in climate impact studies (24).

This includes raw climate model output, as well as model output that has been processed by «bias correction» (removal of some known errors) and / or «downscaling» (addition of finer spatial detail).

Minimize model biases, especially biases that are known to correlate with the climate response of models.

The recent cold, snowy winters are black swans if you listened to the «experts,» like the featherheads at UKMET with their bogus warm - biased models, but if you've the brains and judgment to find the few guys who really know what they're talking about, Bastardi.

Models are known to have large biases in snow and ice cover over high northern latitudes [16].

In spite of well - known biases of tropospheric temperature and humidity in climate models, comparisons indicate that the intermodel range in the rate of clear - sky radiative damping are small despite large intermodel variability in the mean clear - sky OLR.

Prior to 1988, the satellite data that Trenberth uses is not available, but it is known that long term records in radiosondes contain large inhomogeneities due to improving observing systems, increasing spatial resolution (but still very little ocean coverage), and the NCEP data in particular contains large model biases.

To say that the pre-satellite humidity trends are correct, despite the many changes in instrumentation, despite the changes in spatial and temporal resolution (but still almost no ocean coverage), despite the known problems with NCEP model bias, and despite that it has been wrong throughout the satellite era... well, it's ludicrous.

A more accurate model is: politics is a system that 1) selects against skills needed for rigorous thinking and for qualities such as groupthink and confirmation bias, 2) incentivises a badly selected set of people to consider their career not the public interest, 3) drops them into dysfunctional institutions with no relevant training and poor tools, 4) centralises vast amounts of power in the hands of these people and institutions in ways we know are bound to cause huge errors, and 5) provides very weak (and often damaging) feedback so facing reality is rare, learning is practically impossible, and system reform is seen as a hostile act by political parties and civil services worldwide.