DiscreteTimeStateEvolution¶
Discrete-time state evolution via Markov transition distributions.
The next state is drawn from a conditional distribution given the current state, control, and time indices:
\[
x_{t_{k+1}} \sim p\left(x_{t_{k+1}} \mid x_{t_k}, u_{t_k}, t_k, t_{k+1}\right)
\]
Implementations must return a NumPyro-compatible distribution (e.g.,
numpyro.distributions.Distribution) that can be sampled and evaluated.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x
|
State
|
Current state \(x \in \mathbb{R}^{d_x}\). |
required |
u
|
Control | None
|
Current control input or None. |
required |
t_now
|
Time
|
Current time index \(t_k\). |
required |
t_next
|
Time
|
Next time index \(t_{k+1}\) (for non-uniform sampling or continuous-time embeddings). |
required |
Returns:
| Name | Type | Description |
|---|---|---|
DistributionT |
Distribution over the next state \(x_{t_{k+1}}\).
In practice this should be a |