{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/82414"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/82414","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A Bayesian approach to feed reconstruction","abstract":"In this thesis, we developed a Bayesian approach to estimate the detailed composition of an unknown feedstock in a chemical plant by combining information from a few bulk measurements of the feedstock in the plant along with some detailed composition information of a similar feedstock that was measured in a laboratory. The complexity of the Bayesian model combined with the simplex-type constraints on the weight fractions makes it difficult to sample from the resulting high-dimensional posterior distribution. We reviewed and implemented different algorithms to generate samples from this posterior that satisfy the given constraints. 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