Fix docs

Author: pnkraemer

docs/examples_advanced/parameter_estimation_blackjax.py

@@ -236,7 +236,7 @@ def solve_adaptive(theta, *, save_at):
 def logposterior_fn(theta, *, data, ts, obs_stdev=0.1):
     """Evaluate the logposterior-function of the data."""
     solution = solve_fixed(theta, ts=ts)
-    y_T = jax.tree.map(lambda s: s[-1], solution.posterior)
+    y_T = jax.tree.map(lambda s: s[-1], solution.solution_full)
     logpdf_data = strategy.log_marginal_likelihood_terminal_values(
         data, standard_deviation=obs_stdev, posterior=y_T
     )

docs/examples_advanced/parameter_estimation_optax.py

@@ -113,7 +113,7 @@ def parameter_to_data_fit(parameters_, /, standard_deviation=1e-1):
     return -1.0 * strategy.log_marginal_likelihood(
         data,
         standard_deviation=jnp.ones_like(sol_.t) * standard_deviation,
-        posterior=sol_.posterior,
+        posterior=sol_.solution_full,
     )
 
 

docs/examples_basic/taylor_coefficients.py

@@ -115,7 +115,7 @@ def solve(tc):
 key = jax.random.PRNGKey(seed=15)
 _, _, ssm = probdiffeq.prior_wiener_integrated(tcoeffs, ssm_fact="dense")
 strategy = probdiffeq.strategy_filter(ssm)
-posterior = solution.posterior
+posterior = solution.solution_full
 sample_one = strategy.markov_sample(key, posterior, reverse=True)
 sample_many = strategy.markov_sample(key, posterior, shape=(1, 2, 3), reverse=True)