A Markovian Decision Process with hidden states and hidden costs

Costs of Quality: Exploratory Analysis of Hidden Elements and Prioritization using Analytic Hierarchy Process

Cost of Quality analysis is emerged as an effective tool for the industrial managers for pinpointing the deficiencies in the system as well as for identifying the improvement areas by highlighting the cost reduction opportunities. However , this analysis will be fully effective only if it is further extended to identify the cost incurred in ensuring quality in all areas of the supply chain incl...

Hidden Process Models: Decoding Overlapping Cognitive States with Unknown Timing

We use Hidden Process Models (HPMs) to evaluate different models of a functional Magnetic Resonance Imaging (fMRI) study in which subjects decide whether stimuli match. We demonstrate the ability of HPMs to simultaneously estimate the hemodynamic response functions and the onset times of a set of cognitive processes underlying an fMRI time series, and to compare different models in a principled...

Stochastic Games with Hidden States∗

This paper studies infinite-horizon stochastic games in which players observe payoffs and noisy public information about a hidden state each period. Public randomization is available. We find that, very generally, the feasible and individually rational payoff set is invariant to the initial prior about the state in the limit as the discount factor goes to one. We also provide a recursive charac...

costs of quality: exploratory analysis of hidden elements and prioritization using analytic hierarchy process

cost of quality analysis is emerged as an effective tool for the industrial managers for pinpointing the deficiencies in the system as well as for identifying the improvement areas by highlighting the cost reduction opportunities. however , this analysis will be fully effective only if it is further extended to identify the cost incurred in ensuring quality in all areas of the supply chain incl...

Unsupervised Bayesian segmentation using hidden Markovian fields