hypothesis | A Python toolkit for inference | Machine Learning library

I want to create a Coq tactic that looks something like the following. I assert a proposition named H, I prove that proposition, and then I use simpl within that proposition. The tactic would look something like this:

Issue description: Brownie tests containing either

I have set up and using https://github.com/jvm-profiling-tools/async-profiler which is extremely useful but I have a strange thing I cannot explain.

My setup is exactly where multiple presentation showed it can help:

• AKS kubernetes cluster with a nodepool

• A pod deployed on one node

• Within the container I have set up openjdk-11 with the debuginfo

• The profiling setup is a simple ./profiler start -e malloc PID

• Since I'm in a virtualised environment profiling works, the only warning I get is

  ...

I'm working on a decision making analysis where I'm trying to illustrate the trade off between false positive (false go) vs. false negative (false no-go) using R. I have created a density plot with null and alternative hypothesis curves, but would like to further illustrate the relationship via the example plots like these. Would appreciate the help in creating plots like example 1 and 2, especially example 1. Thank you!

Density plot

Example 1

Example 2

My colleagues and I routinely create ad hoc scripts in R, to perform ETL on proprietary data and generate automated reports for clients. I am attempting to standardize our approach, for the sake of consistency, modularity, and reusability.

In particular, I want to consolidate our most commonly used functions in a central directory, and to access them as if they were functions from a proprietary R package. However, I am quite raw as an R developer, and my teammates are even less experienced in R development. As such, the development of a formal package is unfeasible for the moment.

Fortunately, the box package, by Stack Overflow's very own Konrad Rudolph, provides (among other modularity) an accessible approach to approximate the behavior of an R package. Unlike the rigorous development process outlined by the RStudio team, box requires only that one create a regular .R file, in a meaningful location, with roxygen2 documentation (#') and explicit @exports:

Writing modules

The module bio/seq, which we have used in the previous section, is implemented in the file bio/seq.r. The file seq.r is, by and large, a normal R source file, which happens to live in a directory named bio.

In fact, there are only three things worth mentioning:

1. Documentation. Functions in the module file can be documented using ‘roxygen2’ syntax. It works the same as for packages. The ‘box’ package parses the
   documentation and makes it available via box::help. Displaying module help requires that ‘roxygen2’ is installed.

2. Export declarations. Similar to packages, modules explicitly need to declare which names they export; they do this using the annotation comment #' @export in front of the name. Again, this works similarly to ‘roxygen2’ (but does not require having that package installed).

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At the moment, I am tinkering around with a particular module, as "imported" into a script. While the "import" itself works seamlessly, I cannot seem to access the documentation for my functions.

I am experimenting with box on a Lenovo ThinkPad running Windows 10 Enterprise. I have created a script, aptly titled Script.R, whose location serves as my working directory. My module exists in the relative subdirectory ./Resources/Modules as the humble file time.R, reproduced here:

Is it possible to prove this forall (a b : nat), a <=? b = true -> a <=? S b = true. in Coq?

I tried this so far

I need to generate a random sample of size 200 (n=200) from a normal distribution with variance 1 and true mu (average) I specify; then, I test the draw against a hypothesis: mu <= 1. I need to do this for each of 400 potential true thetas, and for each true theta I need to replicate this 200 times.

I already did this for n=1, but I realize my approach is not replicable. For each 400 thetas, I ran the following:

I am trying to use a for-loop over a range of positive and negative values and then plot the results. However, I'm having trouble getting R not to plot the correct values, since the negative values seem to screw up the index.

More precisely, the code I am running is:

I am using the BradleyTerry2 package in R to analyse my data. When using the BTm function to calculate ability scores, the first item in the dataset is removed as a reference, given a score of 0 and then other ability scores are calculated relative to this reference.

Is there a way to use a null hypothesis as a reference, rather than using the first item in the dataset?

This is the code I am using. The "ID" field is player id. This code calculates an ability score for each "Matchup," relative to the first matchup in the dataset.

BTv1 <- BTm(player1=winner,player2=loser,id="ID",formula=~Matchup+(1|ID),data=btmdata)

I am trying to test against the null hypothesis that matchup has no effect on match outcomes, but currently I don't know what ability score corresponds to the null hypothesis. I would like to use this null hypothesis as a reference, rather than using the first matchup in the dataset.

For those wanting to reproduce my results, you can find my files on my university onedrive.