FAQ page first draft

docs/faq.rst

@@ -1,28 +1,93 @@
 Frequently Asked Questions
 **************************
 
-Installation issues
+General Questions
 ===================
 
-Bambi not working on Windows
+Why have a Bayesian regression library?
 ----------------------------
 
-If Bambi does not work on Windows you can...
+Bayesian modelling allows flexible (read 'bespoke') model specification and also provides an
+estimation of uncertainty in the model parameters. Both of these are wildly useful in 
+practice, in particular in a business context where the model is used to make decisions,
+and where a complex model may be needed to capture the underlying relationships. Further,
+Bayesian modelling allows graceful handling of small sample sizes by judicious use of
+prior distributions.
 
-* First
-* Second
 
 And finally, see `this issue <https://github.com/bambinos/bambi/issues/389>`_.
 
 
-Bambi not working on Linux
+I'm not familiar with Bayesian modelling, where can I learn more?
 ----------------------------
 
-* First
-* Second
+There are a number of excellent resources available online, including:
+    * [Bambi examples](https://bambinos.github.io/bambi/examples.html)
+    * [Bayesian Modeling and Computation](https://www.amazon.com/Bayesian-Modeling-Computation-Statistics-Science/dp/0124058884)
+    * [Bayesian Data Analysis](https://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954)
+    * [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)
+
+What is the difference between Bambi and PyMC3?
+----------------------------
+    * Bambi is a wrapper around PyMC3, NumPyro, and BlackJax. It provides a simple interface for
+            specifying Bayesian models, and allows for easy inference using MCMC or 
+            variational inference.
+    * PyMC3 is a library for Bayesian modelling, and is the backend used by Bambi. It is a very
+            powerful library, but can be difficult to use for beginners. Bambi provides a simple
+            interface for specifying models, and allows for easy inference using MCMC or variational
+            inference.
 
 
-Miscellaneous how to
+Inference Questions
 ====================
 
-Pass arguments to ``Model.fit()``...
+My sampler through errors/indicating divergences, what should I do?
+----------------------------
+    * Divergences are a common issue in Bayesian modelling, and are usually not a problem as long as
+            they are not prevalent. However, if you are seeing a lot of divergences, you may want 
+            to try 1) respecifying your model, 2) a different sampler.
+    * If the sampler fails, this is likely an issue with model specification. Make sure you are using
+            the correct priors for your model, and that you are not specifying a prior that is too
+            strong (e.g. a prior that is too narrow), or one that does not match the data (e.g. a
+            prior that doesn't cover the domain of the data such as using a HalfNormal prior for a
+            parameter that can be negative).
+
+What sampling methods are available?
+----------------------------
+The sampler used is automatically selected given the type of variables used in the model.
+For inference, Bambi supports both MCMC and variational inference. MCMC is the default, but you can specify variational inference by passing `inference_method='vi'` to `Model.fit()`.
+Bambi also supports multiple backends for MCMC, including NumPyro, and BlackJax
+(see API for "fit" method for more details [here](https://bambinos.github.io/bambi/api_reference.html)).
+
+My sampler is taking a long time, what should I do?
+----------------------------
+Long sampling times may simply be a result of the model being complex, or the data being
+large. If you are using a sampler that supports parallelization, you can try increasing the
+number of chains and/or cores. If you are using a sampler that does not support parallelization, you can try using a different sampler that does (e.g. NumPyro vs. PyMC3). You can also
+use variational inference, which is much faster than MCMC, but is not as flexible. You can
+also try respecifying your model, or using a different prior.
+
+Can infernece in Bambi be sped up using GPUs/TPUs?
+----------------------------
+Yes, Bambi supports inference on GPUs and TPUs using the numpyro and blackjax backends. 
+See the API for "fit" method for more details 
+[here](https://bambinos.github.io/bambi/api_reference.html).
+
+Model Specification Questions
+====================
+
+My data has a non-normal distributions, can I still use Bambi?
+----------------------------
+Yes, Bambi supports a wide range of distributions which can be specified using the "family"
+argument to the "Model". You can find examples of how to specify these distributions 
+in the [Bambi examples](https://bambinos.github.io/bambi/examples.html).
+
+How do I find out what priors are available?
+----------------------------
+You can find a list of available priors in the [Bambi Models API](https://bambinos.github.io/bambi/api_reference.html#module-bambi.models) under the "family" argument.
+
+Does bambi come with pre-specified regression models?
+----------------------------
+To allow building of bespoke models, Bambi does not come with pre-specified regression models.
+However, you can find examples of how to specify models in the 
+[Bambi examples](https://bambinos.github.io/bambi/examples.html).