Todo List

Class BFL::BFL::MCPdf< T >

This class can and should be made far more efficient!!!

Member BFL::BFL::MCPdf< T >::CumulativePDFGet ()

what's the best way to remove some samples?

Member BFL::BFL::Pdf< T >::CovarianceGet () const

extend this more general to n-th order statistic

Member BFL::BFL::Pdf< T >::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)

Class BFL::BootstrapFilter< StateVar, MeasVar >

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 BFL::ConditionalPdf< Var, CondArg >

Investigate if we can allow. It is for sure that we'll need another class then the std::list to implement this!

Class BFL::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< StateVar, MeasVar >::_timestep

Check wether this really belongs here

Member BFL::Filter< StateVar, MeasVar >::Filter (const Filter< StateVar, MeasVar > &filt)

Check if we should make a copy of the pdf's too?

Class BFL::MCPdf< T >

This class can and should be made far more efficient!!!

Member BFL::MCPdf< T >::CumulativePDFGet ()

what's the best way to remove some samples?

Class BFL::MeasurementModel< MeasVar, StateVar >

Check if there should be a "model" base class...

Class BFL::MixtureBootstrapFilter< StateVar, MeasVar >

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 BFL::NonminimalKalmanFilter

Seriously reimplement this class!

Class BFL::ParticleFilter< StateVar, MeasVar >

: 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).

Member BFL::Pdf< T >::CovarianceGet () const

extend this more general to n-th order statistic

Member BFL::Pdf< T >::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)

Class BFL::SystemModel< T >

Check if there should be a "model" base class...