| ►Nglppl_algos | |
| Clikelihood_weighting | |
| CAncestorMetropolis | Metropolis Hastings using the prior distribution as the proposal |
| Carity | How many input arguments does the largest constructor of the distribution have? |
| Carity< Beta > | |
| Carity< BVNormal > | |
| Carity< Categorical > | |
| Carity< Discrete< T > > | |
| Carity< DiscreteUniform > | |
| Carity< Gamma > | |
| Carity< Normal > | |
| Carity< Parameter< V > > | |
| Carity< Poisson > | |
| Carity< Triangular > | |
| Carity< Uniform > | |
| Carity< WienerProcess > | |
| Carray_ | |
| Cauxiliary_info | |
| Cauxiliary_info< Categorical > | |
| CBeta | A beta distribution parameterized by shape parameters alpha and beta |
| CBVNormal | Bivariate normal in the mean / cholesky parameterization |
| CCategorical | |
| Ccollection_t | |
| CDefaultPolicy | Identity function between sets of distributions |
| CDiscrete | A discrete distribution over an arbitrary collection of items parameterized by a std::vector<double> of probabilities |
| Cdiscrete_ | |
| Cdiscrete_< unsigned long > | |
| CDiscreteUniform | A discrete uniform distribution over integers |
| CDistribution | Abstract base class that can be subclassed for creation of new distributions |
| Cdynamic_bounded | |
| Cdynamic_bounded< double > | |
| CEndog | |
| CExog | |
| CGamma | |
| CGenericMetropolis | Generic Metropolis-Hastings algorithm with user-specified proposal distribution |
| Cgr_output | Product of a gr_pair and the output of a probabilistic program |
| Cgr_pair | |
| Cgraph_ir | A graph intermediate representation of a causal model |
| Cgraph_node | A fundamental data structure of which a graph intermediate representation is composed |
| Cgraph_node_construct | Finds and tracks parents/children of nodes involved in a sample or observe statement |
| Cgraph_observe_node_construct | Creates an observe node in a graph_ir |
| Cgraph_sample_node_construct | Creates a sample node in a graph_ir |
| Chas_proposal | Will the passed class template have an associated proposal distribution? |
| Chas_proposal< GenericMetropolis > | |
| Chas_proposal< ImportanceSampling > | |
| CImportanceSampling | Importance sampling using an arbitrary user-defined proposal distribution |
| Cinf_options_t | Options used by all inference algorithms |
| CInference | Universal base class for inference methods |
| Cinference_state | State that is used by inference algorithms |
| Cinference_state< AncestorMetropolis, I, O, Ts... > | |
| Cinference_state< GenericMetropolis, I, O, Ts... > | |
| Cinference_state< ImportanceSampling, I, O, Ts... > | |
| Cinput_types | What are the types passed as arguments to the largest constructor? |
| Cinput_types< Beta > | |
| Cinput_types< BVNormal > | |
| Cinput_types< Categorical > | |
| Cinput_types< Discrete< T > > | |
| Cinput_types< DiscreteUniform > | |
| Cinput_types< Gamma > | |
| Cinput_types< Normal > | |
| Cinput_types< Parameter< V > > | |
| Cinput_types< Poisson > | |
| Cinput_types< Triangular > | |
| Cinput_types< Uniform > | |
| Cinput_types< WienerProcess > | |
| CLikelihoodWeighting | Likelihood weighting importance sampling, using the prior as the proposal |
| CLogSumExpQ | Computes a streaming log-sum-exp |
| Cmapping | |
| Cmapping< A< T >, A< T > > | |
| Cmapping< B, A > | |
| Cmapping< unbounded< double >, unit_interval< double > > | |
| Cmapping< unbounded< T >, non_negative< T > > | |
| CNo | |
| Cnode_spec | |
| Cnode_t | A fundamental data structure that includes address, distribution, sampled value type, score, whether the value was observed, and a markov process over interpretations |
| CNodeBlock | |
| CNodeCondition | |
| CNodeParameter | |
| CNodePropose | |
| CNodeReplace | |
| CNodeReplay | |
| CNodeStandard | |
| Cnon_negative | |
| CNormal | |
| CNormalPolicy | Every continuous distribution type is approximated by a normal distribution |
| CObs | |
| COptimizer | Optimizes a value function and returns the argmax value |
| Coutput_dim | What are the dimensions of the output of calling .sample(...)? |
| Coutput_domain | In what domain is the output? |
| Coutput_domain< Beta > | |
| Coutput_domain< BVNormal > | |
| Coutput_domain< Categorical > | |
| Coutput_domain< Discrete< T > > | |
| Coutput_domain< Gamma > | |
| Coutput_domain< Normal > | |
| Coutput_domain< Parameter< V > > | |
| Coutput_domain< Poisson > | |
| Coutput_domain< Triangular > | |
| Coutput_domain< WienerProcess > | |
| CParamConstructor | |
| CParameter | |
| Cparameter_match | |
| Cparameter_match< D, Gamma, O, Ts... > | Computes gamma distribution parameter updates from posterior samples |
| Cparameter_match< D, Normal, O, Ts... > | Computes normal distribution parameter updates from posterior samples via moment matching |
| Cparameter_match< Parameter< Constraint< BaseType > >, Parameter< Constraint< BaseType > >, O, Ts... > | Identity function on parameter value |
| CParameterMatching | |
| CParameterMatching< Policy, FilterValueType< O, Ts... >, I, O, Ts... > | Computes a variational approximation to posterior from posterior samples |
| CParameterMatching< Policy, typename ProductGenerator< WeightedMeanStd, std::pair< double, double >, O, Ts... >::EmitType, I, O, Ts... > | Computes a variational approximation to posterior from (mean, standard deviation) pairs |
| Cplate_base_ | |
| CPoisson | |
| CProbability | Computes the empirical probability of an event |
| ►CProductGenerator | Generates a queryer that returns a fully factored collection of views |
| CEmitType | Product of values (mapping from address to result type of underlying queryer) and distributions (mapping from address to first-encountered distribution at that address) |
| CQ | A queryer that emits \(V[p(z|x)] = \prod_a V[p(z_a|x)]\) |
| Cprogram_info | |
| Cprogram_rep | |
| CQueryer | Interface to all querying mechanisms for sample-based inference (and possibly other inference algorithms). Much computation that is associated with inference is implemented by subclasses of Queryer (rather than in inference algorithms themselves, as is often done) |
| Crecord_collection_t | Collects records generated by an inference algorithm into an empirical posterior distribution over records and output values |
| Crecord_t | |
| Crecord_t< DTypes< Ts... > > | A fundamental data structure that holds a mapping from addresses to nodes, an insertion order, and a record-level interpretation |
| CRecordBlock | |
| CRecordBlock< Obs > | |
| CRecordBlock< Sample > | |
| CRecordReplace | |
| CRecordReplay | |
| CRecordRewrite | |
| CRecordStandard | |
| CSample | |
| CScore | |
| Cslice_base_ | |
| Cslice_plate | Represents a vector of \(N\) random variables |
| Cstatic_plate | Represents \(N\) iid random variables |
| Ctranslation | |
| Ctranslation< Categorical > | |
| Ctranslation< Gamma > | |
| Ctranslation< Normal > | |
| Ctranslation< Value< double > > | |
| Ctranslation< Value< int > > | |
| Ctranslation< Value< unsigned > > | |
| Ctranslation< Value< unsigned long > > | |
| CTriangular | |
| Ctyped_map | A unordered map with value type equal to a sum type of input type and one or more distribution types, parameterized by an update Policy |
| Cunbounded | |
| CUniform | A continuous uniform distribution over doubles |
| Cunit_interval | |
| Cunit_interval< double > | |
| CUpdate | Experimental base class of typed_map-based update logic |
| CUpdateFilter | A filtering algorithm that operates on typed_map objects |
| CValue | A minimal wrapper of a value that is tracked in a graph_ir |
| Cvalue_collection_t | A collection of sampled values |
| CWeighted | Access to all sample weights |
| CWeightedMean | Computes the mean of the specified sample site with O(1) memory |
| CWeightedMeanStd | Computes the mean and standard deviation of the specified sample site with O(1) memory |
| CWeightedRecord | A collection of weighted records |
| CWeightedValue | A collection of weighted values from a single site |
| CWeightedValue< std::unique_ptr< value_collection_t< V > >, O, Ts... > | |
| CWienerProcess | |
1.9.1