#!/usr/bin/env python
# coding: utf-8

# # Introduction
# Here are a few notebooks outlining basic functionality of GPy
# * [Handling models](./models_basic.ipynb)
# * [Basic GP regression](./basic_gp.ipynb)
# * [Working with kernels](./basic_kernels.ipynb)

# # Example GPy use
# 
# This set of examples shows some of the functionality of the GPy Gaussian process framework in python. The framework is BSD licensed and we welcome collaborators to develop new functionality.
# 
# ### Multiple Output Gaussian Processes
# * [Coregionalization with Gaussian Processes](./coregionalized_regression_tutorial.ipynb) This tutorial shows the use of a coregionalized model within GPy. In particular such models can be used for multi-task or multi-output learning.
# * [Coregionalization on Marathon Data](./multiple%20outputs.ipynb) This tutorial runs the multioutput regression on a higher level, introducing stacked hierarchical multitask regression.
# 
# ### Different Noise Models
# * [GP classification](./basic_classification.ipynb) A very simple turorial on GP classification.
# * [Count Data with GPy](./Poisson%20regression%20tutorial.ipynb) This tutorial gives an example of Poisson regression using GPy.
# * [Heteroschedastic Gaussian Processes](./heteroscedastic_regression.ipynb) This tutorial shows how heteroschedastic Gaussian processes can be fit using GPy (with an interactive widget!).
# 
# ### Approximations
# * [Sparse Gaussian Processes](./sparse_gp_regression.ipynb) This tutorial gives a quick overview of the variational approximation used to fit sparse Gaussian processes. 
# 
# ### SVI
# * [Stochastic Variational Inference for GP Regression](./SVI.ipynb) A simple demonstration of using SVI to fit a regression model (requires the climin library)
# 
# ### Partially parametric models
# * [Parametric non-parametric Gaussian Process Regression](./ParametricNonParametricInference.ipynb) The steps from Linear regression to non-linear regression and finally non-parametric (better: infinite parametric) regression.
# 
# ### MCMC
# * [Using Hybrid Monte Carlo to infer posterior distributions on kernel parameters](./sampling_hmc.ipynb)
# 
# ### Other apllications
# * [Probabilistic metrics for GP-LVM and Bayesian GP-LVMs](./MagnificationFactor.ipynb) Introduction to the probabilistic geometries approach for latent variable models, with a demonstration of the plotting tool for the visualization of magnification factors with different data sets.

# # Developer and Own modules
# * [Implementing Optimizers](./optimizer-implementation.ipynb)
# * [Integral Kernels](./Integral_kernel.ipynb)

# # GPy Configuration Files
# * [Configuration Files](./config.ipynb) Setting up your local GPy configuration.

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