Model Validation Quotes

Today's scientists have substituted mathematics for experiments, and they wander off through equation after equation, and eventually build a structure which has no relation to reality.

You can build a statistical model and that's all well and good, but if you're dealing with a new type of financial instrument, for example, or a new type of situation - then the choices you're making are pretty arbitrary in a lot of respects.

We think that computer models represent reality especially if we developed the models.

In desperation I asked Fermi whether he was not impressed by the agreement between our calculated numbers and his measured numbers. He replied, "How many arbitrary parameters did you use for your calculations?" I thought for a moment about our cut-off procedures and said, "Four." He said, "I remember my friend Johnny von Neumann used to say, with four parameters I can fit an elephant, and with five I can make him wiggle his trunk." With that, the conversation was over.

Much of my work in this period was concerned with exploring the logic of economic models, but also with attempting to reconcile the models with every day observation.

Computer models of the climate....[are] a very dubious business if you don't have good inputs.

The challenge for what everybody's seeing in deep learning - the software richness is really quite high. In training, you have to wait days and weeks before it comes back to tell you whether your model works or not. And in the beginning, they all don't work.

One of the biggest and most important tools of theoretical physics is the wastebasket.

There was a danger that skeptics and opponents would misread those likelihood ratio tests as rejections of an entire class of models, which of course they were not.

The climate-studies people who work with models always tend to overestimate their models. They come to believe models are real and forget they are only models.

A model is a good model if first it interprets a wide range of observations in terms of a simple and elegant model, and second if the model makes definite predictions that can be tested, and possibly falsified, by observation.

On the whole, the evidence indicates that model trends in the troposphere are very likely inconsistent with observations.

If a proposal doesn't prove out as expected when we run it, sometimes we change the model.

All science is based on models, and every scientific model comprises three distinct stages: statement of well-defined hypotheses; deduction of all the consequences of these hypotheses, and nothing but these consequences; confrontation of these consequences with observed data.

There are no surprising facts, only models that are surprised by facts; and if a model is surprised by the facts, it is no credit to that model.

In the 1980s, there were occasions when it made sense to say, 'it is too difficult to maximize the likelihood function, and besides if we do, it will blow our model out of the water.'