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We propose and test on real data a two-tier estimation strategy for inferring occupancy levels from measurements of CO2 concentration and temperature levels. The first tier is a blind identification step, based either on a frequentist Maximum Likelihood method, implemented using non-linear optimization, or on a Bayesian marginal likelihood method, implemented using a dedicated Expectation-Maximization algorithm. The second tier resolves the ambiguity of the unknown multiplicative factor, anddoi:10.1109/ecc.2015.7330720 dblp:conf/eucc/EbadatBVWHJ15 fatcat:kx7wadpkrjahba42sqblh6s4ce