Time-variable discrete markovian transition kernel for sampling the next state knowing the present state \(x_0\).
Considering a Markov chain \((X_k)_{k\in Z_+}\) with discrete state space \(S\), and given each initial state \(x_0\) of \(S\), defines the markovian probability distribution at time \(t\) : \(P_t(X_1=\cdot|X_0=x_0)\), and samples the stochastic value \(X_1\), according to this probability law.
- Template Parameters
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Distribution | type of the transition kernels. |
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| TransitionKernel () |
| Default constructor.
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| TransitionKernel (state_type const &x, Time const &t, Distribution const &d) |
| Initialize a transition kernel with a state/time couple and its associated distribution.
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| TransitionKernel (state_type const &x, Time const &t, Distribution &&d) |
| Initialize a transition kernel with a state/time couple and its associated distribution.
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| TransitionKernel (const TransitionKernel &)=default |
| Copy constructor.
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| TransitionKernel (TransitionKernel &&)=default |
| Move constructor.
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TransitionKernel< Time, Distribution > & | operator= (const TransitionKernel< Time, Distribution > &)=default |
| Copy assignment operator.
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TransitionKernel< Time, Distribution > & | operator= (TransitionKernel< Time, Distribution > &&)=default |
| Move assignment operator.
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TransitionKernel< Time, Distribution > & | set (state_type const &x, Time const &t, Distribution const &d) |
| Set a probability distribution for a departure state and time.
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TransitionKernel< Time, Distribution > & | set (state_type const &x, Time const &t, Distribution &&d) |
| Set a probability distribution for a departure state and time.
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bool | has_distribution (state_type const &x, Time const &t) const |
| Checks if a probability distribution is associated to a departure state x at time t.
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template<typename Generator > |
state_type | operator() (Generator &g, state_type const &x, Time const &t) |
| Sample an arrival state conditionnaly to departure state and a time.
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