PyMC | A Python Minecraft Server implementation , made for speed | Video Game library

data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data

I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.

I have a website where I document a list of installed pythonic libraries.

For each library, I want to have available:

• The name of the library (obviously)
• A link to the documentation for the library (because documentation is useful)
• A brief description of the library (so people can quickly see what the library does)
• The currently installed version (to stop people asking me "Are you using version x.y?")

My current solution is to use the name as the text of a link, href'd to its documentation, and accept that the version & description are supplementary information, and can be made available to the user using a tool-tip - so they can sit in a title attribute

Example:

To continue my research on how to plot a xml file and continue checking my code, I first applied a division to signal.attrib ["Value"], since it shows some string values ​​and what I'm interested in is the numeric values.

And as you can see below, I relied on the documentation for Pandas and SQL Compare.

I want to make a mixture of two TruncatedNormal distributions in pymc3.

I am trying to modify this piece of documentation. See example #2 for Poisson.

I'm trying to understand how to use a black box likelihood function in pymc. Basically, this is explained here. I have tried implementing this on my own with a very simple Python model (a double logistic function), and no gradient. In addition to the code in the link I mentioned, that sets up the theano wrappers around the loglikelihood function, I have the following code

I'm struggling to understand how observed data works in pymc3. From the information I've found so far, these two examples have been the most helpful for getting me as far as I have, but I can't get my model to work.

As an example of what I'm trying to do, say I have records from customers at a restaurant, recording the temperature of the day on a categorical rating scale out of 5, and whether or not they ordered a main meal, a side, or a beverage. I've set up some mock data like so:

I am trying to visualize simple linear regression with highest posterior density (hpd) for multiple groups. However, I have a problem to apply hpd for each condition. Whenever I ran this code, I am extracting the same posterior density for each condition. I would like to visualize posterior density that corresponds to it's condition. How can I plot hpd for each group?

EDIT: Issue has been solved in PyMC3 discourse

I've been trying to implement and estimate, with pymc3, a basic stochastic volatility (SV) model of the following form:

r_t = exp{h_t/2}*e_t

h_t = r_0 + r_1*h_{t-1} + n_t

where r_t is the return process and h_t the (latent) log-variance process following a AR(1) process. My code (MWE) for this looks as follows:

I am not a user of PyMC myself, but recently I stumbled upon this article that showed a snippet of some PyMC model: