Sequential Monte Carlo methods
GPTKB entity
Statements (32)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:alsoKnownAs |
particle filters
|
| gptkbp:appliesTo |
gptkb:signal_processing
computer vision robotics econometrics tracking problems |
| gptkbp:basedOn |
importance sampling
resampling |
| gptkbp:developedBy |
1990s
|
| gptkbp:handles |
nonlinear systems
non-Gaussian noise |
| https://www.w3.org/2000/01/rdf-schema#label |
Sequential Monte Carlo methods
|
| gptkbp:improves |
gptkb:Rao-Blackwellization
adaptive resampling |
| gptkbp:limitation |
computational cost
particle degeneracy |
| gptkbp:notableFor |
auxiliary particle filter
bootstrap filter sequential importance resampling |
| gptkbp:publishedIn |
gptkb:Doucet,_de_Freitas,_and_Gordon_(2001)_'Sequential_Monte_Carlo_Methods_in_Practice'
|
| gptkbp:relatedTo |
gptkb:Kalman_filter
gptkb:Markov_chain_Monte_Carlo Hidden Markov models |
| gptkbp:requires |
random sampling
weighting of samples resampling step |
| gptkbp:usedFor |
Bayesian filtering
state estimation approximate inference |
| gptkbp:bfsParent |
gptkb:Nicolas_Chopin
|
| gptkbp:bfsLayer |
6
|