docs: update tutorials to use strongly typed build_posterior logic

Author: abelaba

docs/advanced_tutorials/12_iid_data_and_permutation_invariant_embeddings.ipynb

@@ -62,6 +62,7 @@
     "\n",
     "from sbi.analysis import pairplot\n",
     "from sbi.inference import NLE, NPE, simulate_for_sbi\n",
+    "from sbi.inference.posteriors.posterior_parameters import MCMCPosteriorParameters\n",
     "from sbi.simulators.linear_gaussian import (\n",
     "    linear_gaussian,\n",
     "    true_posterior_linear_gaussian_mvn_prior,\n",
@@ -220,17 +221,16 @@
     "nle_samples = []\n",
     "num_samples = 1000\n",
     "\n",
-    "mcmc_parameters = dict(\n",
+    "mcmc_parameters = MCMCPosteriorParameters(\n",
+    "    method=\"slice_np_vectorized\",\n",
     "    num_chains=50,\n",
     "    thin=5,\n",
     "    warmup_steps=30,\n",
     "    init_strategy=\"proposal\",\n",
     ")\n",
-    "mcmc_method = \"slice_np_vectorized\"\n",
     "\n",
     "posterior = inferer.build_posterior(\n",
-    "    mcmc_method=mcmc_method,\n",
-    "    mcmc_parameters=mcmc_parameters,\n",
+    "    posterior_parameters=mcmc_parameters,\n",
     ")\n",
     "\n",
     "# Generate samples with MCMC given the same set of x_os as above.\n",

docs/tutorials/16_implemented_methods.ipynb

@@ -250,16 +250,17 @@
    "outputs": [],
    "source": [
     "from sbi.inference import NLE\n",
+    "from sbi.inference.posteriors.posterior_parameters import MCMCPosteriorParameters\n",
     "\n",
     "inference = NLE(prior)\n",
     "proposal = prior\n",
     "for _ in range(num_rounds):\n",
     "    theta = proposal.sample((num_sims,))\n",
     "    x = simulator(theta)\n",
     "    _ = inference.append_simulations(theta, x).train()\n",
-    "    posterior = inference.build_posterior(mcmc_method=\"slice_np_vectorized\",\n",
-    "                                          mcmc_parameters={\"num_chains\": 20,\n",
-    "                                                           \"thin\": 5})\n",
+    "    posterior = inference.build_posterior(posterior_parameters=MCMCPosteriorParameters(method=\"slice_np_vectorized\", num_chains=20,\n",
+    "                                                           thin=5))\n",
+    "\n",
     "    proposal = posterior.set_default_x(x_o)"
    ]
   },
@@ -279,15 +280,15 @@
    "outputs": [],
    "source": [
     "from sbi.inference import NLE\n",
+    "from sbi.inference.posteriors.posterior_parameters import VIPosteriorParameters\n",
     "\n",
     "inference = NLE(prior)\n",
     "proposal = prior\n",
     "for _ in range(num_rounds):\n",
     "    theta = proposal.sample((num_sims,))\n",
     "    x = simulator(theta)\n",
     "    _ = inference.append_simulations(theta, x).train()\n",
-    "    posterior = inference.build_posterior(sample_with=\"vi\",\n",
-    "                                          vi_method=\"fKL\").set_default_x(x_o)\n",
+    "    posterior = inference.build_posterior(posterior_parameters=VIPosteriorParameters(vi_method=\"fKL\")).set_default_x(x_o)\n",
     "    proposal = posterior.train()  # Train VI posterior on given x_o."
    ]
   },
@@ -364,16 +365,17 @@
    "outputs": [],
    "source": [
     "from sbi.inference import NRE\n",
+    "from sbi.inference.posteriors.posterior_parameters import MCMCPosteriorParameters\n",
     "\n",
     "inference = NRE(prior)\n",
     "proposal = prior\n",
     "for _ in range(num_rounds):\n",
     "    theta = proposal.sample((num_sims,))\n",
     "    x = simulator(theta)\n",
     "    _ = inference.append_simulations(theta, x).train()\n",
-    "    posterior = inference.build_posterior(mcmc_method=\"slice_np_vectorized\",\n",
-    "                                          mcmc_parameters={\"num_chains\": 20,\n",
-    "                                                           \"thin\": 5})\n",
+    "    posterior = inference.build_posterior(posterior_parameters=MCMCPosteriorParameters(method=\"slice_np_vectorized\", num_chains=20,\n",
+    "                                                           thin=5))\n",
+    "\n",
     "    proposal = posterior.set_default_x(x_o)"
    ]
   },

docs/tutorials/Example_01_DecisionMakingModel.ipynb

@@ -86,6 +86,7 @@
     "\n",
     "from sbi.analysis import pairplot\n",
     "from sbi.inference import MNLE, MCMCPosterior\n",
+    "from sbi.inference.posteriors.posterior_parameters import MCMCPosteriorParameters\n",
     "from sbi.inference.potentials.likelihood_based_potential import LikelihoodBasedPotential\n",
     "from sbi.neural_nets import likelihood_nn\n",
     "from sbi.utils import BoxUniform, MultipleIndependent, mcmc_transform\n",
@@ -220,7 +221,7 @@
    "outputs": [],
    "source": [
     "# Build posterior from the trained estimator and prior.\n",
-    "mnle_posterior = trainer.build_posterior(prior=prior, mcmc_parameters=mcmc_kwargs)\n",
+    "mnle_posterior = trainer.build_posterior(prior=prior, posterior_parameters=MCMCPosteriorParameters(**mcmc_kwargs))\n",
     "\n",
     "mnle_samples = mnle_posterior.sample((num_samples,), x=x_o)"
    ]