- Member BFL::Filter::_timestep
- Check wether this really belongs here
- Class BootstrapFilter
- The implementation is very slow for the moment. It would probably be much faster to add a vector<WeightedSample> to the private members of this class.
- Class ConditionalPdf
- Investigate if we can allow. It is for sure that we'll need another class then the std::list to implement this!
- Member BFL::Pdf::SampleFrom (vector< Sample< T > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const
- replace the C-call "void * args" by a more object-oriented structure: Perhaps something like virtual Sample * Sample (const int num_samples,class Sampler)
- Member BFL::Pdf::CovarianceGet () const
- extend this more general to n-th order statistic
- Class DiscreteConditionalPdf
- Check if this is the best way to implement this.
- Member BFL::DiscreteConditionalPdf::DiscreteConditionalPdf (int num_states=1, int num_conditional_arguments=1, int cond_arg_dimensions[]=NULL)
- Get cleaner api and implementation
- Member BFL::Filter::Filter (const Filter< StateVar, MeasVar > &filt)
- Check if we should make a copy of the pdf's too?
- Class MCPdf
- This class can and should be made far more efficient!!!
- Member BFL::MCPdf::CumulativePDFGet ()
- what's the best way to remove some samples?
- Class MeasurementModel
- Check if there should be a "model" base class...
- Class NonminimalKalmanFilter
- Seriously reimplement this class!
- Class ParticleFilter
- : Actually all particle filters represented by this class are of the "Sequential importance sampling methods" type. Typical of those methods is the so called Proposal density. In theory it would be possible to create Filters using a recursive version of other Monte Carlo methods (eg. MCMC methods), although I am not aware of any of these (due to the increased complexity).
- Class SystemModel
- Check if there should be a "model" base class...
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