docs(optimizr): add logo and fix rtd deps

docs/source/algorithms/mcmc.md

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+# MCMC Sampling
+
+Metropolis-Hastings sampler for Bayesian inference with Rust acceleration.
+
+## Usage
+
+```python
+import numpy as np
+from optimizr import mcmc_sample
+
+# Log-likelihood of a Gaussian model
+
+def log_likelihood(params, data):
+    mu, sigma = params
+    residuals = (data - mu) / sigma
+    return -0.5 * np.sum(residuals**2) - len(data) * np.log(sigma)
+
+observations = np.random.randn(1000) + 1.2
+samples = mcmc_sample(
+    log_likelihood_fn=log_likelihood,
+    data=observations,
+    initial_params=np.array([0.0, 1.0]),
+    param_bounds=[(-5, 5), (0.1, 5.0)],
+    n_samples=8000,
+    burn_in=500,
+    proposal_std=0.2,
+)
+
+print("Posterior mean:", samples.mean(axis=0))
+```
+
+## Tips
+- Keep `proposal_std` modest to maintain acceptance rate (20–40%).
+- `burn_in` should be at least 5–10% of total samples for stable chains.
+- Provide tight `param_bounds` to avoid exploring invalid regions.