| adapt(double accept_prob, const kjb::Vector &previous_state, const kjb::Vector &proposed_state, const kjb::Vector &accepted_state) | Simple_adaptive_mh_step< kjb::Vector > | inlinevirtual |
| Generic_adaptive_mh_step(const To_vector &to_vector, const From_vector &from_vector, const Target_distribution &target, const kjb::Matrix &initial_covariance, double goal_accept_rate) | Generic_adaptive_mh_step< Model > | inline |
| Generic_adaptive_mh_step(const Generic_adaptive_mh_step &other) | Generic_adaptive_mh_step< Model > | inline |
| get_cholesky_covariance() const | Simple_adaptive_mh_step< kjb::Vector > | inline |
| get_current_gamma() const | Simple_adaptive_mh_step< kjb::Vector > | inline |
| get_global_scale() const | Simple_adaptive_mh_step< kjb::Vector > | inline |
| get_log_lambda() const | Simple_adaptive_mh_step< kjb::Vector > | inline |
| operator()(Model &in, double lt_m) | Generic_adaptive_mh_step< Model > | inline |
| Simple_adaptive_mh_step< kjb::Vector >::operator()(kjb::Vector &in, double lt_m) | Simple_adaptive_mh_step< kjb::Vector > | inline |
| Proposer typedef | Generic_adaptive_mh_step< Model > | |
| set_constant_learning_rate(double gamma, size_t change_point=0) | Simple_adaptive_mh_step< kjb::Vector > | inline |
| set_inverse_learning_rate(double C, double alpha) | Simple_adaptive_mh_step< kjb::Vector > | inline |
| set_post_callback(const boost::function1< void, const Self & > &cb) | Simple_adaptive_mh_step< kjb::Vector > | inline |
| set_target(const Target_distribution &t) | Simple_adaptive_mh_step< kjb::Vector > | inlineprotected |
| set_temperature(double t) | Simple_adaptive_mh_step< kjb::Vector > | inline |
| Simple_adaptive_mh_step(const Target_distribution &target, const kjb::Matrix &initial_covariance, double goal_accept_rate) | Simple_adaptive_mh_step< kjb::Vector > | inline |
| Target_distribution typedef | Generic_adaptive_mh_step< Model > | |
1.8.6