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diff --git a/docs/build/doctrees/tutorial.doctree b/docs/build/doctrees/tutorial.doctree
index 4b294bebc104a62246ac7f4aefbbd7a7b5b7e550..833e4dc22bef16f11722f47cb0b4fc93478530fc 100644
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diff --git a/docs/build/html/_sources/beam.rst.txt b/docs/build/html/_sources/beam.rst.txt
index f64f800e07737abc85220c52ed45c31a46f760dd..61db6729de4cd5656e1adb5b0c0e8aafcb84ba7e 100644
--- a/docs/build/html/_sources/beam.rst.txt
+++ b/docs/build/html/_sources/beam.rst.txt
@@ -6,9 +6,6 @@ input file can be linked with the bayesvalidrox package.
 
 The surrogate is trained without active learning and no inference is performed, though reference data is available.
 
-.. warning::
-   This example does not run through without issues at the moment.
-
 Model and Data
 ==============
 
diff --git a/docs/build/html/_sources/borehole.rst.txt b/docs/build/html/_sources/borehole.rst.txt
index fba2214482c589a8eb7e5dfb8c277d1326f2b0f4..36cdfd325e659fbf346080951ba2decfdc5b0129 100644
--- a/docs/build/html/_sources/borehole.rst.txt
+++ b/docs/build/html/_sources/borehole.rst.txt
@@ -30,8 +30,6 @@ You will see how to check the quality of your regression model and perform sensi
 no reference data given
 Surrogate with AL - OMP for regression and Space-filling sequential exploitaiton scheme (no data)
 
-.. warning::
-   Still some error with saving the sobol indices
 
 Model and Data
 ==============
diff --git a/docs/build/html/_sources/ishigami.rst.txt b/docs/build/html/_sources/ishigami.rst.txt
index 8bb5639db3c4ef92e6edcb104194e390622ce65c..8dfe217f3338e7ec4a4f8539d21bafcab750735d 100644
--- a/docs/build/html/_sources/ishigami.rst.txt
+++ b/docs/build/html/_sources/ishigami.rst.txt
@@ -29,9 +29,6 @@ sensitivity analysis via Sobol Indices.
 
 No reference data is given for this example, the surrogate is trained with BCS as the regression method and no active learning.
 
-.. warning::
-   Results in error messages due to regression method in 0.0.5 - will be fixed in next release
-
 Model and Data
 ==============
 
diff --git a/docs/build/html/_sources/model_description.rst.txt b/docs/build/html/_sources/model_description.rst.txt
index f08ea0cd2ab6eb368f290d6121ce2e60fa1d35c8..bb719886b9a5a61cfed3f44eebcf810b5ca46ef8 100644
--- a/docs/build/html/_sources/model_description.rst.txt
+++ b/docs/build/html/_sources/model_description.rst.txt
@@ -6,7 +6,6 @@ Models
    .. container:: leftside
    
       BayesValidRox gives options to create interfaces for a variety of models with the class :any:`bayesvalidrox.pylink.pylink.PyLinkForwardModel`.
-	  
 	  Its main function is to run the model on given samples and to read in and contain MC references and observations.
 	  
 	  Models can be defined via python functions, shell commands or as general executables.
diff --git a/docs/build/html/_sources/packagedescription.rst.txt b/docs/build/html/_sources/packagedescription.rst.txt
index 30164f68ce3cc9fab8eddc107552d004951a245c..f245959b8d5a4b1d56de4e9cc86cff64b2da9b98 100644
--- a/docs/build/html/_sources/packagedescription.rst.txt
+++ b/docs/build/html/_sources/packagedescription.rst.txt
@@ -31,9 +31,6 @@ The current master can be installed by cloning the repository.
 
 Overview
 ========
-.. note::
-   #TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.
-
 This package is split into multiple aspects corresponding to its folder structure.
 
 
diff --git a/docs/build/html/_sources/surrogate_description.rst.txt b/docs/build/html/_sources/surrogate_description.rst.txt
index 1055fccb86df67983dd7f4d75c072b5dae989c7a..0d17b06ceb08848c9b4e6471ab331673f8a7c2d3 100644
--- a/docs/build/html/_sources/surrogate_description.rst.txt
+++ b/docs/build/html/_sources/surrogate_description.rst.txt
@@ -88,9 +88,6 @@ For this the sampling method should be set to 'user' and our samples given as ``
 Now we create an engine object with the model, experimental design and surrogate model.
 With the function ``start_engine`` the engine performs its preparations for training.
 
-.. warning::
-   The function start_engine will likely be deprecated in future releases.
-
 >>> Engine_ = Engine(MetaMod, Model, ExpDesign)
 >>> Engine_.start_engine()
 
diff --git a/docs/build/html/_sources/tutorial.rst.txt b/docs/build/html/_sources/tutorial.rst.txt
index 468c50adccfb0a9fecb53a862a5b478d8f442a9d..5ad40b9dd379fe260f8f7d334a65a6ac212d0c5d 100644
--- a/docs/build/html/_sources/tutorial.rst.txt
+++ b/docs/build/html/_sources/tutorial.rst.txt
@@ -140,7 +140,6 @@ The experimental design provides instructions on how to sample the input paramet
 Various sampling methods are available, and the samples can also be given by the user.
 
 >>> ExpDesign = ExpDesign(Inputs)
->>> ExpDesign.Method = 'normal'
 >>> ExpDesign.n_init_samples = 100
 >>> ExpDesign.sampling_method = 'latin_hypercube'
 
@@ -172,10 +171,6 @@ Sequential training
 -------------------
 The basic surrogate training that we just performed is done only on one static set of data.
 **bayesvalidrox** also provide the option of sequential training, also known as active learning, where additional samples to be trained on are chosen by the surrogate.
-This can be activated by setting the ``method`` of the experimental design as ``'sequential'``.
-
->>> ExpDesign.method = 'sequential'
-
 This will split the training into two parts.
 In the first part the training is performed as before, though the 
 size of this initial training set can a bit smaller.
@@ -183,7 +178,7 @@ size of this initial training set can a bit smaller.
 >>> ExpDesign.n_init_samples = 3*ndim
 >>> ExpDesign.sampling_method = 'latin_hypercube'
 
-The options for sequential training are listed at ...........
+The options for sequential training are listed in the dedicated page of the :any:`packagedescription`.
 New samples are set by exploration and exploitation.
 Exploration refers to samples that are randomly drawn from the prior input space, 
 while exploitation can use different metrics.
@@ -201,16 +196,14 @@ The tradeoff between the two helps to avoid overfitting, while keeping the faste
 >>> ExpDesign.n_cand_groups = 4
 
 
-Here we set the exploitaiton method to be Bayesian Active Learning .....cite......
-, which chooses the new samples based on the information gain with respect to some given data, here the model results described earlier.
+Here we set the exploitaiton method to be Bayesian Active Learning, which chooses the new samples based on the information gain with respect to some given data, here the model results described earlier.
 
 In addition we need to set the information metric to use, here ``'DKL'`` is chosen.
 
 >>> ExpDesign.exploit_method = 'BayesActDesign'
 >>> ExpDesign.util_func = 'DKL'
 
-This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values 
-with standard deviations that are as large as the values themselves.
+This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values with standard deviations that are as large as the values themselves.
 
 >>> obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
 >>> DiscrepancyOpts = Discrepancy('')
@@ -277,11 +270,11 @@ Inverse parameter estimation can be done in **bayesvalidrox** with the class :an
 
 If we set ``emulator`` to be true the Bayesian Inference will be performed based on the emulator.
 Some posterior predictions will be plotted by setting ``plot_post_pred``.
-More options for Bayesian inference are listed at .....
+More options for Bayesian inference are listed at :any:`bayes_description`.
 
 .. note::
    Setting ``emulator = False`` means that the inference is based on actual model runs and not the surrogate.
-   However, there might still be some bugs here,
+   This can also be achieved by initializing the Engine without a surrogate object.
 
 >>> BayesOpts.emulator = True
 >>> BayesOpts.plot_post_pred = True
@@ -309,7 +302,7 @@ For this tutorial we assume the uncertainty of the data to be distributed accord
 >>> BayesOpts.measurement_error = obsData
 
 .. warning::
-   This option might be deprecated quite soon or not work at all?
+   This option will become deprecated.
 
 **Option II:** Set discrepancy distributions all at once
 
diff --git a/docs/build/html/beam.html b/docs/build/html/beam.html
index 0e46e422fbe551a000fa9e8338cf32ca8a2dfc5e..6c82f54b9c2f2bca66cd21adf154c9aa11a04319 100644
--- a/docs/build/html/beam.html
+++ b/docs/build/html/beam.html
@@ -341,10 +341,6 @@
 illustrates how a model with an executable and an
 input file can be linked with the bayesvalidrox package.</p>
 <p>The surrogate is trained without active learning and no inference is performed, though reference data is available.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>This example does not run through without issues at the moment.</p>
-</div>
 <section id="model-and-data">
 <h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
 <div class="table-wrapper colwidths-given docutils container" id="id1">
diff --git a/docs/build/html/borehole.html b/docs/build/html/borehole.html
index 459a2f5eede4dd2557c17f0b12f760eeabbfa4a5..eafbe8618ff8d0757196ae0777a1cf723a5fffbe 100644
--- a/docs/build/html/borehole.html
+++ b/docs/build/html/borehole.html
@@ -364,10 +364,6 @@ General Public License for more details.</p>
 </div></blockquote>
 <p>no reference data given
 Surrogate with AL - OMP for regression and Space-filling sequential exploitaiton scheme (no data)</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Still some error with saving the sobol indices</p>
-</div>
 <section id="model-and-data">
 <h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
 <div class="table-wrapper colwidths-given docutils container" id="id1">
diff --git a/docs/build/html/ishigami.html b/docs/build/html/ishigami.html
index 56739d967363aec5e5aa336104279ddcdcbee1a1..06d5b7d8c9adcb37841b53c2a5e31c6cdb19a142 100644
--- a/docs/build/html/ishigami.html
+++ b/docs/build/html/ishigami.html
@@ -366,10 +366,6 @@ General Public License for more details.</p>
 </dl>
 </div></blockquote>
 <p>No reference data is given for this example, the surrogate is trained with BCS as the regression method and no active learning.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Results in error messages due to regression method in 0.0.5 - will be fixed in next release</p>
-</div>
 <section id="model-and-data">
 <h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
 <div class="table-wrapper colwidths-given docutils container" id="id1">
diff --git a/docs/build/html/model_description.html b/docs/build/html/model_description.html
index d7460501bc0c677cfaaaaba31cd5cfda45aa4507..2dbaeed956d41cede70ba75d52434aab60a16698 100644
--- a/docs/build/html/model_description.html
+++ b/docs/build/html/model_description.html
@@ -339,12 +339,12 @@
 <h1>Models<a class="headerlink" href="#models" title="Link to this heading">¶</a></h1>
 <div class="twocol docutils container">
 <div class="leftside docutils container">
-<p>BayesValidRox gives options to create interfaces for a variety of models with the class <a class="reference internal" href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.pylink.pylink.PyLinkForwardModel</span></code></a>.</p>
-<blockquote>
-<div><p>Its main function is to run the model on given samples and to read in and contain MC references and observations.</p>
+<dl>
+<dt>BayesValidRox gives options to create interfaces for a variety of models with the class <a class="reference internal" href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.pylink.pylink.PyLinkForwardModel</span></code></a>.</dt><dd><p>Its main function is to run the model on given samples and to read in and contain MC references and observations.</p>
 <p>Models can be defined via python functions, shell commands or as general executables.
 This allows for the use of BayesValidRox with a broad range of models and easy extension to models that are defined with e.g. UM-Bridge.</p>
-</div></blockquote>
+</dd>
+</dl>
 </div>
 <div class="rightside docutils container">
 <a class="reference internal image-reference" href="_images/model.png"><img alt="UML diagram for the bayesvalidrox class :any:`bayesvalidrox.pylink.pylink.PyLinkForwardModel`." src="_images/model.png" style="width: 150px;" /></a>
diff --git a/docs/build/html/packagedescription.html b/docs/build/html/packagedescription.html
index 66819e77f7eef8c7c7924b8131ea89426cffc523..c76923ed2587b0b106908c8890e2adb48ed50353 100644
--- a/docs/build/html/packagedescription.html
+++ b/docs/build/html/packagedescription.html
@@ -360,10 +360,6 @@ pip<span class="w"> </span>install<span class="w"> </span>.
 </div>
 <section id="overview">
 <h3>Overview<a class="headerlink" href="#overview" title="Link to this heading">¶</a></h3>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>#TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.</p>
-</div>
 <p>This package is split into multiple aspects corresponding to its folder structure.</p>
 <a class="reference internal image-reference" href="_images/folder_structure.png"><img alt="Folder structure of **bayesvalidrox**" src="_images/folder_structure.png" style="width: 600px;" /></a>
 <p>The folder <code class="docutils literal notranslate"><span class="pre">surrogate_models</span></code> contains all the functions and classes that are necessary in order to create and train the surrogate model.
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index cd3af9ca8735687c9a3f1d1d50be88c9466e3569..3aaa01c41f479cebaf4856492681920308cc6705 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"API": [[66, "api"]], "Active learning: iteratively expanding the training set": [[64, "active-learning-iteratively-expanding-the-training-set"]], "Arguments": [[29, "arguments"], [42, "arguments"], [42, "id2"]], "Attributes": [[3, "attributes"], [5, "attributes"], [7, "attributes"], [9, "attributes"], [13, "attributes"], [16, "attributes"], [25, "attributes"], [26, "attributes"], [39, "attributes"], [42, "attributes"], [47, "attributes"], [49, "attributes"], [50, "attributes"], [52, "attributes"], [55, "attributes"], [58, "attributes"], [60, "attributes"]], "Bayesian Inference": [[81, "bayesian-inference"]], "Bayesian inference and multi-model comparison": [[67, "bayesian-inference-and-multi-model-comparison"]], "Contribution": [[71, "contribution"]], "Define probabilistic input model": [[81, "define-probabilistic-input-model"]], "Define surrogate (meta) model": [[81, "define-surrogate-meta-model"]], "Define the data uncertainty": [[81, "define-the-data-uncertainty"]], "Define the model with PyLinkForwardModel": [[81, "define-the-model-with-pylinkforwardmodel"]], "Discrepancy": [[65, "id3"], [68, "id3"]], "EXAMPLES": [[70, "examples"]], "Example": [[64, "example"], [72, "example"], [74, "example"], [79, "example"], [80, "example"]], "Example: Analytical function": [[65, "example-analytical-function"]], "Example: OHagan-function": [[76, "example-ohagan-function"]], "Example: beam": [[68, "example-beam"]], "Example: borehole": [[69, "example-borehole"]], "Example: ishigami": [[73, "example-ishigami"]], "Example: model comparison": [[75, "example-model-comparison"]], "Example: pollution": [[78, "example-pollution"]], "Examples": [[49, "examples"]], "Exploration, exploitation and tradeoff": [[64, "exploration-exploitation-and-tradeoff"]], "Further contents": [[71, "further-contents"]], "Import necessary libraries": [[81, "import-necessary-libraries"]], "Indices and tables": [[71, "indices-and-tables"]], "Installation": [[71, "installation"], [77, "installation"]], "License": [[71, "license"]], "Links": [[71, "links"]], "MetaModel options": [[80, "metamodel-options"]], "MetaModel settings": [[65, "id4"], [68, "id4"], [69, "id3"], [73, "id3"], [75, "id7"], [76, "id3"], [78, "id3"]], "Model 1: L2_model": [[75, "model-1-l2-model"]], "Model 1: NL2_model": [[75, "model-1-nl2-model"]], "Model 1: NL4_model": [[75, "model-1-nl4-model"]], "Model and Data": [[65, "model-and-data"], [68, "model-and-data"], [69, "model-and-data"], [73, "model-and-data"], [76, "model-and-data"], [78, "model-and-data"]], "Models": [[74, "models"]], "Note": [[39, "note"], [60, "note"]], "Notes": [[24, "notes"], [25, "notes"], [26, "notes"], [52, "notes"], [55, "notes"]], "Overview": [[77, "overview"]], "Parameters": [[3, "parameters"], [3, "id2"], [3, "id4"], [3, "id7"], [5, "parameters"], [5, "id1"], [5, "id3"], [5, "id5"], [5, "id7"], [5, "id9"], [5, "id11"], [5, "id13"], [5, "id15"], [7, "parameters"], [9, "parameters"], [9, 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\ No newline at end of file
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27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], "tabl": 71, "theori": [], "tradeoff": 64, "train": [64, 65, 68, 69, 73, 75, 76, 78, 80, 81], "tutori": 81, "uncertainti": 81, "update_precis": 56, "user": 77, "valid": 71, "vblinearregress": 26, "within_rang": 17}})
\ No newline at end of file
diff --git a/docs/build/html/surrogate_description.html b/docs/build/html/surrogate_description.html
index 5067bcefc9a84dd6dafa11384999af1e0f878a90..c48db82a4773b839e34ca20dce1c610bb97ffb3a 100644
--- a/docs/build/html/surrogate_description.html
+++ b/docs/build/html/surrogate_description.html
@@ -408,10 +408,6 @@ For this the sampling method should be set to ‘user’ and our samples given a
 </div>
 <p>Now we create an engine object with the model, experimental design and surrogate model.
 With the function <code class="docutils literal notranslate"><span class="pre">start_engine</span></code> the engine performs its preparations for training.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>The function start_engine will likely be deprecated in future releases.</p>
-</div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">Engine_</span> <span class="o">=</span> <span class="n">Engine</span><span class="p">(</span><span class="n">MetaMod</span><span class="p">,</span> <span class="n">Model</span><span class="p">,</span> <span class="n">ExpDesign</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Engine_</span><span class="o">.</span><span class="n">start_engine</span><span class="p">()</span>
 </pre></div>
diff --git a/docs/build/html/tutorial.html b/docs/build/html/tutorial.html
index 083bf46bb6b33f719bc6e733066076c5f64f7efc..820f6d348870c77e961ce0e23f2158a2279322da 100644
--- a/docs/build/html/tutorial.html
+++ b/docs/build/html/tutorial.html
@@ -467,7 +467,6 @@ A value of 1 results in standard truncation of the expansion, while smaller valu
 <p>The experimental design provides instructions on how to sample the input parameter space for training and evaluating the surrogate.
 Various sampling methods are available, and the samples can also be given by the user.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span> <span class="o">=</span> <span class="n">ExpDesign</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">Method</span> <span class="o">=</span> <span class="s1">&#39;normal&#39;</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">100</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">&#39;latin_hypercube&#39;</span>
 </pre></div>
@@ -501,18 +500,14 @@ This can be easily read in to avoid retraining the surrogate.</p>
 <h2>Sequential training<a class="headerlink" href="#sequential-training" title="Link to this heading">¶</a></h2>
 <p>The basic surrogate training that we just performed is done only on one static set of data.
 <strong>bayesvalidrox</strong> also provide the option of sequential training, also known as active learning, where additional samples to be trained on are chosen by the surrogate.
-This can be activated by setting the <code class="docutils literal notranslate"><span class="pre">method</span></code> of the experimental design as <code class="docutils literal notranslate"><span class="pre">'sequential'</span></code>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;sequential&#39;</span>
-</pre></div>
-</div>
-<p>This will split the training into two parts.
+This will split the training into two parts.
 In the first part the training is performed as before, though the
 size of this initial training set can a bit smaller.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">ndim</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">&#39;latin_hypercube&#39;</span>
 </pre></div>
 </div>
-<p>The options for sequential training are listed at ………..
+<p>The options for sequential training are listed in the dedicated page of the <a class="reference internal" href="packagedescription.html"><span class="doc">USER GUIDE</span></a>.
 New samples are set by exploration and exploitation.
 Exploration refers to samples that are randomly drawn from the prior input space,
 while exploitation can use different metrics.
@@ -529,15 +524,13 @@ The tradeoff between the two helps to avoid overfitting, while keeping the faste
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_cand_groups</span> <span class="o">=</span> <span class="mi">4</span>
 </pre></div>
 </div>
-<p>Here we set the exploitaiton method to be Bayesian Active Learning …..cite……
-, which chooses the new samples based on the information gain with respect to some given data, here the model results described earlier.</p>
+<p>Here we set the exploitaiton method to be Bayesian Active Learning, which chooses the new samples based on the information gain with respect to some given data, here the model results described earlier.</p>
 <p>In addition we need to set the information metric to use, here <code class="docutils literal notranslate"><span class="pre">'DKL'</span></code> is chosen.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">exploit_method</span> <span class="o">=</span> <span class="s1">&#39;BayesActDesign&#39;</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">util_func</span> <span class="o">=</span> <span class="s1">&#39;DKL&#39;</span>
 </pre></div>
 </div>
-<p>This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values
-with standard deviations that are as large as the values themselves.</p>
+<p>This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values with standard deviations that are as large as the values themselves.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">obsData</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">Model</span><span class="o">.</span><span class="n">observations</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">Model</span><span class="o">.</span><span class="n">Output</span><span class="o">.</span><span class="n">names</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">DiscrepancyOpts</span> <span class="o">=</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s1">&#39;Gaussian&#39;</span>
@@ -603,11 +596,11 @@ The method <code class="docutils literal notranslate"><span class="pre">sobolInd
 </div>
 <p>If we set <code class="docutils literal notranslate"><span class="pre">emulator</span></code> to be true the Bayesian Inference will be performed based on the emulator.
 Some posterior predictions will be plotted by setting <code class="docutils literal notranslate"><span class="pre">plot_post_pred</span></code>.
-More options for Bayesian inference are listed at …..</p>
+More options for Bayesian inference are listed at <a class="reference internal" href="bayes_description.html"><span class="doc">Bayesian inference and multi-model comparison</span></a>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Setting <code class="docutils literal notranslate"><span class="pre">emulator</span> <span class="pre">=</span> <span class="pre">False</span></code> means that the inference is based on actual model runs and not the surrogate.
-However, there might still be some bugs here,</p>
+This can also be achieved by initializing the Engine without a surrogate object.</p>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">emulator</span> <span class="o">=</span> <span class="kc">True</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">plot_post_pred</span> <span class="o">=</span> <span class="kc">True</span>
@@ -637,7 +630,7 @@ For this tutorial we assume the uncertainty of the data to be distributed accord
 </div>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
-<p>This option might be deprecated quite soon or not work at all?</p>
+<p>This option will become deprecated.</p>
 </div>
 <p><strong>Option II:</strong> Set discrepancy distributions all at once</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">DiscrepancyOpts</span> <span class="o">=</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
diff --git a/docs/source/beam.rst b/docs/source/beam.rst
index f64f800e07737abc85220c52ed45c31a46f760dd..61db6729de4cd5656e1adb5b0c0e8aafcb84ba7e 100644
--- a/docs/source/beam.rst
+++ b/docs/source/beam.rst
@@ -6,9 +6,6 @@ input file can be linked with the bayesvalidrox package.
 
 The surrogate is trained without active learning and no inference is performed, though reference data is available.
 
-.. warning::
-   This example does not run through without issues at the moment.
-
 Model and Data
 ==============
 
diff --git a/docs/source/borehole.rst b/docs/source/borehole.rst
index fba2214482c589a8eb7e5dfb8c277d1326f2b0f4..36cdfd325e659fbf346080951ba2decfdc5b0129 100644
--- a/docs/source/borehole.rst
+++ b/docs/source/borehole.rst
@@ -30,8 +30,6 @@ You will see how to check the quality of your regression model and perform sensi
 no reference data given
 Surrogate with AL - OMP for regression and Space-filling sequential exploitaiton scheme (no data)
 
-.. warning::
-   Still some error with saving the sobol indices
 
 Model and Data
 ==============
diff --git a/docs/source/ishigami.rst b/docs/source/ishigami.rst
index 8bb5639db3c4ef92e6edcb104194e390622ce65c..8dfe217f3338e7ec4a4f8539d21bafcab750735d 100644
--- a/docs/source/ishigami.rst
+++ b/docs/source/ishigami.rst
@@ -29,9 +29,6 @@ sensitivity analysis via Sobol Indices.
 
 No reference data is given for this example, the surrogate is trained with BCS as the regression method and no active learning.
 
-.. warning::
-   Results in error messages due to regression method in 0.0.5 - will be fixed in next release
-
 Model and Data
 ==============
 
diff --git a/docs/source/model_description.rst b/docs/source/model_description.rst
index f08ea0cd2ab6eb368f290d6121ce2e60fa1d35c8..bb719886b9a5a61cfed3f44eebcf810b5ca46ef8 100644
--- a/docs/source/model_description.rst
+++ b/docs/source/model_description.rst
@@ -6,7 +6,6 @@ Models
    .. container:: leftside
    
       BayesValidRox gives options to create interfaces for a variety of models with the class :any:`bayesvalidrox.pylink.pylink.PyLinkForwardModel`.
-	  
 	  Its main function is to run the model on given samples and to read in and contain MC references and observations.
 	  
 	  Models can be defined via python functions, shell commands or as general executables.
diff --git a/docs/source/packagedescription.rst b/docs/source/packagedescription.rst
index 30164f68ce3cc9fab8eddc107552d004951a245c..f245959b8d5a4b1d56de4e9cc86cff64b2da9b98 100644
--- a/docs/source/packagedescription.rst
+++ b/docs/source/packagedescription.rst
@@ -31,9 +31,6 @@ The current master can be installed by cloning the repository.
 
 Overview
 ========
-.. note::
-   #TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.
-
 This package is split into multiple aspects corresponding to its folder structure.
 
 
diff --git a/docs/source/surrogate_description.rst b/docs/source/surrogate_description.rst
index 1055fccb86df67983dd7f4d75c072b5dae989c7a..0d17b06ceb08848c9b4e6471ab331673f8a7c2d3 100644
--- a/docs/source/surrogate_description.rst
+++ b/docs/source/surrogate_description.rst
@@ -88,9 +88,6 @@ For this the sampling method should be set to 'user' and our samples given as ``
 Now we create an engine object with the model, experimental design and surrogate model.
 With the function ``start_engine`` the engine performs its preparations for training.
 
-.. warning::
-   The function start_engine will likely be deprecated in future releases.
-
 >>> Engine_ = Engine(MetaMod, Model, ExpDesign)
 >>> Engine_.start_engine()
 
diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst
index 468c50adccfb0a9fecb53a862a5b478d8f442a9d..5ad40b9dd379fe260f8f7d334a65a6ac212d0c5d 100644
--- a/docs/source/tutorial.rst
+++ b/docs/source/tutorial.rst
@@ -140,7 +140,6 @@ The experimental design provides instructions on how to sample the input paramet
 Various sampling methods are available, and the samples can also be given by the user.
 
 >>> ExpDesign = ExpDesign(Inputs)
->>> ExpDesign.Method = 'normal'
 >>> ExpDesign.n_init_samples = 100
 >>> ExpDesign.sampling_method = 'latin_hypercube'
 
@@ -172,10 +171,6 @@ Sequential training
 -------------------
 The basic surrogate training that we just performed is done only on one static set of data.
 **bayesvalidrox** also provide the option of sequential training, also known as active learning, where additional samples to be trained on are chosen by the surrogate.
-This can be activated by setting the ``method`` of the experimental design as ``'sequential'``.
-
->>> ExpDesign.method = 'sequential'
-
 This will split the training into two parts.
 In the first part the training is performed as before, though the 
 size of this initial training set can a bit smaller.
@@ -183,7 +178,7 @@ size of this initial training set can a bit smaller.
 >>> ExpDesign.n_init_samples = 3*ndim
 >>> ExpDesign.sampling_method = 'latin_hypercube'
 
-The options for sequential training are listed at ...........
+The options for sequential training are listed in the dedicated page of the :any:`packagedescription`.
 New samples are set by exploration and exploitation.
 Exploration refers to samples that are randomly drawn from the prior input space, 
 while exploitation can use different metrics.
@@ -201,16 +196,14 @@ The tradeoff between the two helps to avoid overfitting, while keeping the faste
 >>> ExpDesign.n_cand_groups = 4
 
 
-Here we set the exploitaiton method to be Bayesian Active Learning .....cite......
-, which chooses the new samples based on the information gain with respect to some given data, here the model results described earlier.
+Here we set the exploitaiton method to be Bayesian Active Learning, which chooses the new samples based on the information gain with respect to some given data, here the model results described earlier.
 
 In addition we need to set the information metric to use, here ``'DKL'`` is chosen.
 
 >>> ExpDesign.exploit_method = 'BayesActDesign'
 >>> ExpDesign.util_func = 'DKL'
 
-This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values 
-with standard deviations that are as large as the values themselves.
+This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values with standard deviations that are as large as the values themselves.
 
 >>> obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
 >>> DiscrepancyOpts = Discrepancy('')
@@ -277,11 +270,11 @@ Inverse parameter estimation can be done in **bayesvalidrox** with the class :an
 
 If we set ``emulator`` to be true the Bayesian Inference will be performed based on the emulator.
 Some posterior predictions will be plotted by setting ``plot_post_pred``.
-More options for Bayesian inference are listed at .....
+More options for Bayesian inference are listed at :any:`bayes_description`.
 
 .. note::
    Setting ``emulator = False`` means that the inference is based on actual model runs and not the surrogate.
-   However, there might still be some bugs here,
+   This can also be achieved by initializing the Engine without a surrogate object.
 
 >>> BayesOpts.emulator = True
 >>> BayesOpts.plot_post_pred = True
@@ -309,7 +302,7 @@ For this tutorial we assume the uncertainty of the data to be distributed accord
 >>> BayesOpts.measurement_error = obsData
 
 .. warning::
-   This option might be deprecated quite soon or not work at all?
+   This option will become deprecated.
 
 **Option II:** Set discrepancy distributions all at once