Large margin training for hidden Markov models with partially observed states

2009 ◽  
Author(s):  
Trinh-Minh-Tri Do ◽  
Thierry Artières
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Large Margin ◽  
Partially Observed

scholarly journals A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Anita Jeyam ◽  
Rachel S. McCrea ◽  
Roger Pradel
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Canada Geese ◽  
State Structure ◽  
Data Set ◽  
Partial Observations ◽  
Partially Observed ◽  
Capture Recapture ◽  
Special Case

Hidden Markov models (HMMs) are being widely used in the field of ecological modeling, however determining the number of underlying states in an HMM remains a challenge. Here we examine a special case of capture-recapture models for open populations, where some animals are observed but it is not possible to ascertain their state (partial observations), whilst the other animals' states are assigned without error (complete observations). We propose a mixture test of the underlying state structure generating the partial observations, which assesses whether they are compatible with the set of states observed in the complete observations. We demonstrate the good performance of the test using simulation and through application to a data set of Canada Geese.

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