diff --git a/bpd/likelihood.py b/bpd/likelihood.py
index 2b93d9d..efbed1b 100644
--- a/bpd/likelihood.py
+++ b/bpd/likelihood.py
@@ -9,7 +9,7 @@
 
 def shear_loglikelihood(
     g: Array,
-    post_params: dict[str, Array],
+    post_params: dict[str, Array] | Array,
     *,
     logprior: Callable,
     interim_logprior: Callable,  # fixed
diff --git a/bpd/pipelines.py b/bpd/pipelines.py
index af9e97a..ae1e4a4 100644
--- a/bpd/pipelines.py
+++ b/bpd/pipelines.py
@@ -9,11 +9,13 @@
 
 from bpd.chains import run_inference_nuts
 from bpd.likelihood import shear_loglikelihood
-from bpd.prior import ellip_prior_e1e2
+from bpd.prior import ellip_prior_e1e2, true_ellip_logprior
 from bpd.sample import sample_noisy_ellipticities_unclipped
 
 
-def logtarget_shear(g: Array, *, data: Array, loglikelihood: Callable, sigma_g: float):
+def logtarget_shear(
+    g: Array, *, data: Array | dict[str, Array], loglikelihood: Callable, sigma_g: float
+):
     loglike = loglikelihood(g, post_params=data)
     logprior = stats.norm.logpdf(g, loc=0.0, scale=sigma_g).sum()
     return logprior + loglike
@@ -55,6 +57,44 @@ def pipeline_shear_inference(
     return g_samples
 
 
+def pipeline_shear_inference_simple(
+    rng_key: PRNGKeyArray,
+    e_post: Array,
+    init_g: Array,
+    *,
+    sigma_e: float,
+    sigma_e_int: float,
+    n_samples: int,
+    initial_step_size: float,
+    sigma_g: float = 0.01,
+    n_warmup_steps: int = 500,
+    max_num_doublings: int = 2,
+):
+    _logprior = lambda e, g: true_ellip_logprior(e, g, sigma_e=sigma_e)
+    _interim_logprior = lambda e: jnp.log(ellip_prior_e1e2(e, sigma=sigma_e_int))
+
+    _loglikelihood = partial(
+        shear_loglikelihood, logprior=_logprior, interim_logprior=_interim_logprior
+    )
+    _loglikelihood_jitted = jit(_loglikelihood)
+
+    _logtarget = partial(
+        logtarget_shear, loglikelihood=_loglikelihood_jitted, sigma_g=sigma_g
+    )
+
+    _do_inference = partial(
+        run_inference_nuts,
+        data=e_post,
+        logtarget=_logtarget,
+        n_samples=n_samples,
+        n_warmup_steps=n_warmup_steps,
+        max_num_doublings=max_num_doublings,
+        initial_step_size=initial_step_size,
+    )
+
+    return _do_inference(rng_key, init_g)
+
+
 def logtarget_images(
     params: dict[str, Array],
     data: Array,