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#bayesian

2 posts2 participants0 posts today

Dear R community, I'd like to poll your opinions and ideas about the arguments of a possible R function:

Suppose you're working with the variates of some population; for instance the variates `species`, `island`, `bill_len`, `bill_dep`, `body_mass`, etc. of the `penguins` dataset <cran.r-project.org/package=bas>.

Suppose there's a package that allows you to calculate conditional probabilities of single or joint variates; for example

Pr( bill_len > 40, species = 'Adelie'  |  bill_dep < 16, body_mass = 4200)

and note in particular that this probability refers to intervals/tails ("bill_len > 40") as well as to point-values ("body_mass = 4200").

In fact the crucial point here is that with this function you can inquiry about the probability of a point value, "=", or about a cumulative probability, ">" or "<", or mixtures thereof, as you please.

Now what would be the "best" way to input this kind of choice as an argument to the function? Let's say you have the following two input ways:

**A: indicate the request of a cumulative probability in the variate name:**

```
Pr(
Y = list('bill_len>' = 40, species = 'Adelie'),
X = list('bill_dep<' = 16, body_mass = 4200)
)
```

**B: indicate the request of a cumulative probability in a separate function argument:**

```
Pr(
Y = list(bill_len = 40, species = 'Adelie'),
X = list(bill_dep = 16, body_mass = 4200),
tails = list(bill_len = '>', bill_dep = '<') # or +1, -1 instead of '>', '<'?
)
```

Any other ideas? Feel free to comment :) See <pglpm.github.io/inferno/refere> for a clearer idea about such a function.

Thank you so much for your help!

cran.r-project.orgbasepenguins: Convert Files that Use 'palmerpenguins' to Work with 'datasets'From 'R' 4.5.0, the 'datasets' package includes the penguins and penguins_raw data sets popularised in the 'palmerpenguins' package. 'basepenguins' takes files that use the 'palmerpenguins' package and converts them to work with the versions from 'datasets' ('R' >= 4.5.0). It does this by removing calls to library(palmerpenguins) and making the necessary changes to column names. Additionally, it provides helper functions to define new files paths for saving the output and a directory of example files to experiment with.

Sunken British superyacht Bayesian is raised from the seabed.

A superyacht that sank off the coast of the Italian island of Sicily last year has been raised from the seabed by a specialist salvage team.

Seven of the 22 people on board died in the sinking, including the vessel's owner, British tech tycoon Mike Lynch and his 18-year-old daughter.

The cause of the sinking is still under investigation.

mediafaro.org/article/20250620

BBC · Sunken British superyacht Bayesian is raised from the seabed.By BBC
#Italy#UK#Bayesian

aeon.co/essays/no-schrodingers

This is a pretty good article for showing how confused the interpretation of QM is. And its a good article to understand why i personally side with Bohm and Bell in thinking the pilot wave theory is the one most reasonable to believe. Because the pilot wave theory has the following quality. The theory is a mapping from initial position at time t=0 to final position at time t=1...Its deterministic, but our knowledge of the initial condition is not
#quantum #bohm #bayesian

AeonNo, Schrödinger’s cat is not alive and dead at the same time | Aeon EssaysThe weird paradox of Schrödinger’s cat has found a lasting popularity. What does it mean for the future of quantum physics?

Britische Experten enthüllen: Die Luxusjacht „Bayesian“ kenterte vor Sizilien durch extreme Winde 🌬️⚓. Trotz „unsinkbar“-Ruf war sie bei 130 km/h Wind instabil. 7 Tote, darunter Besitzer Mike Lynch. Bergung pausiert nach tödlichem Unfall bei Taucharbeiten 🤿⚠️. #Bayesian #YachtUnglück #Sizilien #MaritimeSafety #newz

n-tv.de/panorama/Britische-Exp

n-tv NACHRICHTEN · Britische Experten nennen Gründe für Untergang der Luxusjacht "Bayesian"By n-tv NACHRICHTEN

Small advertisement for my Ph.D. thesis and code, focused on #computervision for #robotics.
Using #julialang to implement #Bayesian inference algorithms for the 6D pose estimation of known objects in depth images.
TLDR: it works even with occlusions; needs <1sec on a GPU; does not need training; future research could focus on including color images / semantic information since SOA performs much better if color images are available.
doc: publications.rwth-aachen.de/re
code: github.com/rwth-irt/BayesianPo