stan | The master branch contains

After some trials and errors and some research, I realized that it is actually better to use Helm (Kubernetes package manager) to have a complete and correct manifest for things like NATS or Kafka or ElasticSearch, etc... So, after installing Helm Kubernetes package manager you can install NATS in your Kubernetes cluster by running a command as simple as "helm install my-nats nats/nats" and then you have NATS up and running there in your Kubernetes.

But in this case with the manifest I came up with for NATS unlike before that it was common to connect to NATS server from node.js client using localhost and external access was possible, you can connect to NATS server from node.js client by using service name like this, that is perhaps even more appropriate:

As Phil Mentioned, Edit your code to this and you'll be fine

The problem is not so much that the distribution of errors isn't normal (which is the assumption in a regular linear regression), but that that's clearly not a linear relationship between x and y. You DO have a linear relationship with normally distributed noise (z = x * 10 + noise, where I use z to avoid confusion with your y), but then you apply the softmax function: y = softmax(z). If you want to model this using a linear regression, you need to invert the softmax (i.e. get the z back from y), which you do using the inverse softmax (which is the logit function since the softmax is the inverse logit function and the inverse inverse logit is the logit). Then you can do a standard linear regression.

It looks like the following should work for you, grouping to remove duplicates and ordering based on null

Here is how the log PDF could be adapted. This way, index arrays for subsetting y into censored and non-censored observations can be passed.

Your code in the likelihood section of the model block doesn't match the way you have defined the gpareto...() functions in the functions block. The gpareto functions take a vector as the first argument but instead you are looping through and trying to pass a single element of value each time. That's why you get the error that the data types you are passing to gpareto_lcdf() do not match the "signature" of the function. The function expects the first argument to be a vector, the second to be a real, and the third to be a real. But you are passing three reals.

The error has nothing to do with sigma. The ^ symbol is pointing to the entire function call to gpareto_lcdf() and just happens to be pointing near where the word sigma is, but the error isn't related to sigma.

To fix this error, you would need to do one of the following:

• Redefine the gpareto() functions to take three real arguments and keep your loop in the model block as is.
• Rewrite your model block to not use a loop and instead be vectorized.

I'm not sure the vectorization will work with the condition you have in the model block so you may be forced to go with the first solution.

I would recommend posting this question on the Stan forum where you may get a better answer.

The "trick" seems to indicate in a vector ("indices" here) which value of P to take (1,1,1,1,1,...2,2,2,2,2,...,3,3,3,3,3) and then to loop over it in the parameters to assign the correct value :

i would like to convert EAP pattern to TAP pattern.

That would work, if the API was written with the EAP pattern. But it's not. Instead, it's doing its own kind of subscription pattern, using a delegate to trigger updates.

So, your options are:

1. Map the custom subscription pattern into a standard Observable pattern.
2. Build a buffer (e.g., System.Threading.Channels), have the event handler add items to the buffer, and then consume them as an asynchronous stream.

With both of these options, be sure to handle the "unsubscription" logic as well.

Try to replace: