del Aguila Pla, PolBoquet-Pujadas, AleixJalden, Joakim2023-02-272023-02-272023-02-272022-01-0110.1109/LSP.2022.3233001https://infoscience.epfl.ch/handle/20.500.14299/195188WOS:000915831400004A 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.Engineering, Electrical & ElectronicEngineeringquantization (signal)data modelsdetectorsbiological system modelingprogrammable logic arrayssemiconductor device modelingprobability density functionbayesian statisticslikelihoodprivacy-aware data analysis1-bit compressed sensinginverse problemsperformance analysisconcavityrecoverytext::journal::journal article::research article