nu | weirdly terse stack-based programming language | Compiler library

This is a two-part question:

• what priors should I use for a multivariate random effect where the likelihood is concentrated at small values? (I am assuming that this is the reason you are looking for an alternative to the default inverse Wishart priors) [more suitable for CrossValidated]
• which of these are available in MCMCglmm, and how do I implement them there? [good for Stack Overflow]

The general trick is to decompose the covariance matrix into a multivariate component (the correlation matrix or inverse correlation matrix or something) and a vector of scaling parameters for the standard deviations (or inverse standard deviations); Lemoine suggests U(0,100) for the scaling priors, which I think is bad (why flat? I can't get to the precise page of Gelman and Hill 2007 where they discuss which distribution to use for scaling priors ... but I would be a little surprised if they actually recommended a uniform distribution on the variance scale ...)

update having actually looked at your code (!): I think you're doing the right thing, except that nu=0.002 seems really extreme; see end for that discussion.

This is basically what MCMCglmm does, but it uses a different (IMO better) choice for the scaling priors. It sounds scary:

These priors are all from the non-central scaled F-distribution, which implies the prior for the standard deviation is a non-central folded scaled t-distribution (Gelman, 2006).

but it boils down to choosing four parameters, only two of which you really have to think about.

• V: the prior mean variance (or the prior mean covariance matrix, if you have a multivariate random effect term). According to the course notes, "without loss of generality V can be set to one" (or in the case of a multivariate model, to an identity matrix)
• alpha.mu: we almost always want this to be zero (or as in your example, a vector of zeros); that way the prior for the standard deviation will be a Student t distribution. (There may be a use case for alpha.mu != 0, but I've never run across it.)
• alpha.V: with V set to 1 (or an identity matrix), this is the prior mean of the covariance matrix. A diagonal matrix with a reasonable scale for your problem is a good choice
• nu: the shape parameter; as nu → ∞ we get a half-Normal prior for the standard deviations, with nu=1 we get a Cauchy distribution. Smaller values have fatter tails (less conservative/allowing broader samples, but also giving more danger of weird sampling behaviour in the tails).

For the univariate case Hadfield says the t prior with V=1 is

Here is a functional equivalent of the main Model forward(x) method. It is much more verbose, but it is "unravelling" the flow of operations, making it more easily understandable.

I assumed that the length of the list-arguments are always 5 (i is in the [0, 4] range, inclusive) so I could unpack properly (and it follows the default set of parameters).

If you take a look at the intl-tel-input documentation regarding Initialisation Options. There is an option called excludeCountries.

We can modify your initialisation code to include this option to exclude China:

To convert the JSON string into Python objects you can use:

So you can actually just specify that in the select. Note that it's best practise to include the thing you group by in the select, but since your question is so specific then...

If you wrap the elements in a div (or other containing element) you can use flexbox or grid to align them:

Thank you, everyone. I have solved the issue. If anyone gets similar error here is the solution: In my case the error was because these three variables:

The exception is caused because your FXML file specifies fx:controller="application.LoginController". This will cause the FXMLLoader to create an instance of application.LoginController by (effectively) calling its no-argument constructor. However, your LoginController class doesn't have a no-argument constructor: hence the exception:

Rules for memory allocation.

• Do not allocate so much RAM that swapping will occur. Swapping is terrible for MySQL/MariaDB performance.
• Do adjust innodb_buffer_pool_size such that most of RAM is in use during normal time and even for spikes in activity. (I often say "set it to 70% of available RAM", but you are asking for more details.)
• Do not bother changing other settings; they add to the complexity of "getting it right".

There are 3 situations (based on innodb_buffer_pool_size and dataset size):

• Tiny dataset -- buffer_pool is bigger than necessary --> wasting some of RAM, but so what; it is not useful for anything else. And it give you some room for growth.
• Medium-sized dataset -- Most activity is done in RAM; the system will run nicely.
• Big dataset -- The system may be I/O-bound. Adding RAM is a costly and brute force solution. However, some software techniques (eg, better indexes) may help, such as this for WordPress and WooCommerce.