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updated examples section in docs
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‎README.md‎

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## Quick Start
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### Simple python function
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```python
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from BOBE import BOBE
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‎docs/build/html/py-modindex.html‎

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<a href="BOBE.html#module-BOBE.acquisition"><code class="xref">BOBE.acquisition</code></a></td><td>
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<a href="api/acquisition.html#module-BOBE.acquisition"><code class="xref">BOBE.acquisition</code></a></td><td>
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<a href="BOBE.html#module-BOBE.clf"><code class="xref">BOBE.clf</code></a></td><td>
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<a href="api/gp_models.html#module-BOBE.clf"><code class="xref">BOBE.clf</code></a></td><td>
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<a href="api/core.html#module-BOBE.optim"><code class="xref">BOBE.optim</code></a></td><td>
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‎docs/build/html/searchindex.js‎

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‎docs/source/api/core.rst‎

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BOBEResults Class
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-----------------
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Results container for BOBE optimization runs.
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.. autoclass:: BOBE.utils.results.BOBEResults
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:members:
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Optimization Functions
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----------------------
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.. automodule:: BOBE.optim
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‎docs/source/api/index.rst‎

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API Reference
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=============
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Complete API documentation for all BOBE modules.
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Complete API documentation for all BOBE modules (work in progress).
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.. toctree::
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:maxdepth: 2
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:maxdepth: 1
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core
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acquisition
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gp_models
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likelihood
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samplers
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optim
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utils
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‎docs/source/api/optim.rst‎

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Optimization
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============
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Optimization Functions
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----------------------
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.. automodule:: BOBE.optim
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‎docs/source/api/samplers.rst‎

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Sampling the likelihood surrogate
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==================================
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JaxBo provides interfaces for nested sampling via Dynesty and HMC using NUTS from NumPyro.
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BOBE provides interfaces for nested sampling via Dynesty and HMC using NUTS from NumPyro.
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These are used for sampling the Gaussian Process surrogate of the likelihood function to calculate the evidence or generate posterior samples.
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Nested Sampling

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