Maximum A Posteriori Predecessors in State Observation Models
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
INFORMATION TECHNOLOGIES AND MATHEMATICAL MODELLING
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Maximum A Posteriori (MAP) inference in state observation
models typically covers decoding either marginal MAP state estimates
or the joint MAP state sequence estimate. This paper addresses a novel
yet fundamental MAP inference method denoted as predecessor decoding. This method recursively decodes the most probable predecessors of
a chosen initial state using only the marginal distributions from a forward filtering pass. We elaborate on the motivations, abstract relations
and analogues, and in particular, the differences between marginal MAP,
joint MAP, and MAP predecessors. We conclude by comparing recent results, where predecessor decoding has been utilized for Gaussian mixture
models.
Forschungs-