.. SIPPI documentation master file, created by sphinx-quickstart on Fri Feb 8 10:00:10 2019. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. ``prior``: The a priori model ============================= A priori information is defined by the ``prior`` Matlab structure. Any number of different types of a priori models can be defined. For example a 1D uniform prior can be defined in ``prior{1}``, and 2D Gaussian prior can be defined in ``prior{2}``. Once a prior data structure has been defined (see examples below), a realization from the prior model can be generated using :: m=sippi_prior(prior); The realization from the prior can be visualized using :: sippi_plot_prior(prior,m); A sample (many realizations) from the prior can be visualized using :: m=sippi_plot_prior_sample(prior); All a priori model types in SIPPI allow to generate a new model in the vicinity of a current model using :: [m_new,prior]=sippi_prior(prior,m); in such a way that the prior model will be sampled if the process is repeated (see `Sequential Gibbs Sampling <#sec_seq_gibbs>`__). Types of a priori models ------------------------ Six types of a priori models are available, and can be selected by setting the ``type`` in the ``prior`` structure using e.q. ``prior{1}.type='gaussian'``. The `UNIFORM `__ type prior specifies an uncorrelated ND uniform model. The `GAUSSIAN `__ type prior specifies a 1D generalized Gaussian model. The `FFTMA `__ type prior specifies a 1D-3D Gaussian type a priori model based on the FFT Moving Average method, which is very efficient for unconditional sampling, and for defining a prior Gaussian model with variable/uncertain mean, variance, ranges, and rotation. The `CHOLESKY `__ type prior specifies a 1D-3D Gaussian type a priori model based on Cholesky decomposition of the covariance model. The `VISIM `__ type prior model specifies 1D-3D Gaussian models, utilizing both sequential Gaussian simulation (SGSIM) and direct sequential simulation (DSSIM) that can be conditioned to data of both point- and volume support and linear average data. The `PLURIGAUSSIAN `__ type prior model specifies 1D-3D pluriGaussian. It is a type if truncated Gaussian model that can be used for efficient simulation of categorical values. The `VORONOI `__ type prior defines a number of Voronois cells in a 1D to 3D grid. The `MPS `__ type prior model specifies a 1D-3D multiple-point-based statistical prior model, based on the `MPS <#>`__ C++ library. Simulation types includes SNESIM (based on a search tree or list), ENESIM, and GENESIM (generalized ENESIM). The `SNESIM `__ type prior model specifies a 1D-3D multiple-point-based statistical prior model based on the SNESIM code from `Stanford/SCRF <#>`__. The `SNESIM\_STD `__ is similar to the 'SNESIM' type prior, but is based on `SGEMS <#SGEMS>`__. The following sectionsdocuments the properties of each type of prior model. Examples of using different types of prior models or combining prior models can be found in the `examples section <#sec_ex_prior>`__. .. toctree:: :maxdepth: 1 :caption: Contents: SequentialGibbs.rst uniform.rst gaussian.rst fftma.rst visim.rst cholesky.rst plurigaussian.rst voronoi.rst mps.rst snesim.rst snesim_std.rst