Functions | |
void | addMeasurement (HybridBayesNet &hbn, Key z_key, Key x_key, double sigma) |
std::pair< double, double > | approximateDiscreteMarginal (const HybridBayesNet &hbn, const HybridGaussianConditional::shared_ptr &hybridMotionModel, const VectorValues &given, size_t N=100000) |
Approximate the discrete marginal P(m1) using importance sampling. More... | |
HybridBayesNet | CreateBayesNet (const HybridGaussianConditional::shared_ptr &hybridMotionModel, bool add_second_measurement=false) |
Create two state Bayes network with 1 or two measurement models. More... | |
static HybridGaussianConditional::shared_ptr | CreateHybridMotionModel (double mu0, double mu1, double sigma0, double sigma1) |
Create hybrid motion model p(x1 | x0, m1) More... | |
DiscreteKey | m1 (M(1), 2) |
void test_two_state_estimation::addMeasurement | ( | HybridBayesNet & | hbn, |
Key | z_key, | ||
Key | x_key, | ||
double | sigma | ||
) |
Definition at line 379 of file testHybridGaussianFactor.cpp.
std::pair<double, double> test_two_state_estimation::approximateDiscreteMarginal | ( | const HybridBayesNet & | hbn, |
const HybridGaussianConditional::shared_ptr & | hybridMotionModel, | ||
const VectorValues & | given, | ||
size_t | N = 100000 |
||
) |
Approximate the discrete marginal P(m1) using importance sampling.
Create importance sampling network q(x0,x1,m) = p(x1|x0,m1) q(x0) P(m1), using q(x0) = N(z0, sigmaQ) to sample x0.
Definition at line 425 of file testHybridGaussianFactor.cpp.
HybridBayesNet test_two_state_estimation::CreateBayesNet | ( | const HybridGaussianConditional::shared_ptr & | hybridMotionModel, |
bool | add_second_measurement = false |
||
) |
Create two state Bayes network with 1 or two measurement models.
Definition at line 402 of file testHybridGaussianFactor.cpp.
|
static |
Create hybrid motion model p(x1 | x0, m1)
Definition at line 386 of file testHybridGaussianFactor.cpp.
DiscreteKey test_two_state_estimation::m1 | ( | M(1) | , |
2 | |||
) |