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Convex Quantization Preserves Logconcavity
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DOI：10.1109/LSP.2022.3233001.png)
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Abstract
En 中文
A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization. Quantization is often disregarded when writing likelihood models, ignoring the limitations of the physical detectors used to collect the data. These two facts call for the question: would including quantization in likelihood models preclude logconcavity? are the true data likelihoods logconcave? We provide a general proof that the same simple assumption that leads to logconcave continuous-data likelihoods also leads to logconcave quantized-data likelihoods, provided that convex quantization regions are used.
Keywords:
Quantization (signal)
Data models
Detectors
Biological system modeling
Programmable logic arrays
Semiconductor device modeling
Probability density function
Bayesian statistics
likelihood
privacy-aware data analysis
1-bit compressed sensing
inverse problems
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
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Cited Papers
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