To sample from a compressor, you generate a sequence of random bits and feed it into the decompressor component. If the compressor is very well-suited to Manhattan images, the output of this process will be synthetic images that resemble the real city images. If you try to sample from a standard image compressor, you will just get a greyish haze.
I call this the veridical simulation principle. It is useful because it allows a researcher to detect the ways in which a model is deficient. If the model doesn’t handle shadows correctly, the researcher will realize this when the sampling process produces an image of a tree that casts no shade.
To sample from a compressor, you generate a sequence of random bits and feed it into the decompressor component. If the compressor is very well-suited to Manhattan images, the output of this process will be synthetic images that resemble the real city images. If you try to sample from a standard image compressor, you will just get a greyish haze.
I call this the veridical simulation principle. It is useful because it allows a researcher to detect the ways in which a model is deficient. If the model doesn’t handle shadows correctly, the researcher will realize this when the sampling process produces an image of a tree that casts no shade.
OK, that makes sense. It’s isomorphic to doing model checking by looking data generated by your model.