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#probability

1 post1 participant0 posts today
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kazé

Dear LazyWeb: is there a C/C++, #RustLang or #Zig equivalent of #SciPy’s `stats` module for statistical analysis? Namely:
• a collection of common PDFs (probability density functions);
• MLE (maximum likelihood estimation) for these common distributions;
• KDE (kernel density estimation).

SciPy’s API is a pleasure to work with. Anything that comes close but usable from C/C++/Rust/Zig would make my life so much easier. Boosts appreciated for visibility.

#statistics#probability#DataScience
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Sylvia Wenmackers 🦉🍀

The impact #probability for #2024YR4 has been revised downward in the past days to well below 1%.

I wonder if showing how these impact probabilities change over time also helps to convince people that they should switch doors in the Monty Hall problem. 🚪🚪🐐

The analogy can be strengthened if you take into account that looking for the asteroid in a particular direction and *not* seeing it may also lower the impact probability. #PhilSci

Animation from blogs.esa.int/rocketscience/20

GIF
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Sylvia Wenmackers 🦉🍀

Conference: Probability in Philosophy and Science at the University of Graz 🇦🇹, September 24-26.
It will be a long train journey, but I will speaking there; I don't think any of the other speakers are active on Mastodon.
🎯 More info on the event: philevents.org/event/show/1315
🎲 Call for papers (deadline April 30): philevents.org/event/show/1315
#PhilSci #QuantumPhysics #Probability #Epistemology

philevents.orgProbability in Philosophy and ScienceProbabilities permeate all aspects of our lives. The beliefs we form, the various risks we assess, the epistemic uncertainty we account for, and the decisions we make typically depend on how likely we take some relevant states of the world to be. As finite subjects living in a vast world, we are constantly facing various forms of uncertainty, and it is our experiences that provide us with our most direct, though still limited, access to the world. Given the limited and perspectival character of our fundamental justifiers, i.e. our experiences, our beliefs (and the justification we have for them) seem to come in degrees. Many identify such degrees of belief, or credences, with subjective probabilities. On the other hand, it seems that probability can be interpreted in a more objective way as well. One’s subjective probability assignments may be internally consistent and yet strike us as objectively unjustified or inadequate. Accordingly, some aim to account for probability in epistemic terms that are less subjective than belief, linking it to evidence or justification. Finally, and perhaps most widely assumed, there is the view that probability is a feature of the world itself: probabilities are completely independent from any subject but are out there in the world. This view seems to align well with certain branches of science such as statistics or quantum mechanics. Of course, we can also be pluralists about probability, allowing for both subjective and objective forms of probabilities. However, despite this widespread and apparently intuitive distinction, the details of this overall picture still remain widely debated. What is more, the role probability plays in science remains strongly contested. For instance, quantum mechanics is one of the most fundamental scientific theories, but it is far from clear how we are supposed to interpret quantum probabilities. For some, they are prime examples of objective probabilities; others advocate a thoroughly subjective interpretation. On top of that, various researchers working on reconstructing quantum theory from information-theoretic principles have come to the conclusion that quantum theory fundamentally is a theory of probability. This conference has three interrelated aims: to 1) interrogate the nature and epistemological implications of probability, 2) address the role of probability in science, and 3) assess the epistemic, formal, and pragmatic norms governing our probability assignments. Issues we wish to discuss include, but are not limited to: - the nature of probability; - pluralism about probability; - the relation between probability and concepts such as belief, experience, and justification; - the constraints on rational probability assignments; - the relation between probability and reality; - phenomenological approaches to probability; - the place of probability in action; - the role that probabilities play in the sciences; - the interpretation of quantum probabilities.
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Ross Kang

A post of @11011110 has reminded me that (after a year and a half lurking here) it's never too late for me to toot and pin an intro here.

I am a Canadian mathematician in the Netherlands, and I have been based at the University of Amsterdam since 2022. I also have some rich and longstanding ties to the UK, France, and Japan.

My interests are somewhere in the nexus of Combinatorics, Probability, and Algorithms. Specifically, I like graph colouring, random graphs, and probabilistic/extremal combinatorics. I have an appreciation for randomised algorithms, graph structure theory, and discrete geometry.

Around 2020, I began taking a more active role in the community, especially in efforts towards improved fairness and openness in science. I am proud to be part of a team that founded the journal, Innovations in Graph Theory (igt.centre-mersenne.org/), that launched in 2023. (That is probably the main reason I joined mathstodon!) I have also been a coordinator since 2020 of the informal research network, A Sparse (Graphs) Coalition (sparse-graphs.mimuw.edu.pl/), devoted to online collaborative workshops. In 2024, I helped spearhead the MathOA Diamond Open Access Stimulus Fund (mathoa.org/diamond-open-access).

Until now, my posts have mostly been about scientific publishing and combinatorics.

#introduction
#openscience
#diamondopenaccess
#scientificpublishing
#openaccess
#RemoteConferences
#combinatorics
#graphtheory
#ExtremalCombinatorics
#probability

igt.centre-mersenne.orgInnovations in Graph Theory Innovations in Graph Theory
Replied in thread
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Eric Maugendre

@data @datadon 🧵

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: statisticseasily.com/kendall-t

LEARN STATISTICS EASILY · Kendall Tau-b vs Spearman: Which Correlation Coefficient Wins?Discover why Kendall Tau-b vs Spearman Correlation is crucial for your data analysis and which coefficient offers the most reliable results.
#normality#normalDistribution#modeling
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🐜🦅

My long-in-preparation Cambridge Element ‘Probability and Inductive Logic’ is now available.

doi.org/10.1017/9781009210171

Abstract: Reasoning from inconclusive evidence, or 'induction', is central to science and any applications we make of it. For that reason alone it demands the attention of philosophers of science. This element explores the prospects of using probability theory to provide an inductive logic: a framework for representing evidential support. Constraints on the ideal evaluation of hypotheses suggest that the overall standing of a hypothesis is represented by its probability in light of the total evidence, and incremental support, or confirmation, indicated by the hypothesis having a higher probability conditional on some evidence than it does unconditionally. This proposal is shown to have the capacity to reconstruct many canons of the scientific method and inductive inference. Along the way, significant objections are discussed, such as the challenge of inductive scepticism, and the objection that the probabilistic approach makes evidential support arbitrary.

Cambridge CoreProbability and Inductive LogicCambridge Core - Logic - Probability and Inductive Logic
#probability#induction#bayesianism
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Sylvia Wenmackers 🦉🍀

I read about the 1-in-83 (>1% !) odds of a decent-sized #asteroid (2024 YR4) hitting Earth in 2032. ☄️ space.com/180-foot-asteroid-1-
First thought: "Not now, large space rock." 😬
But soon after, I wondered: how do they determine this #probability? 🤔

Turns out it's a bit like weather forecasts: they run multiple simulations (variations on the measured data) and report the fraction of how often a certain event happens (rain/collision course). #2024yr4 1/2

Space · Astronomers discover 196-foot asteroid with 1-in-83 chance of hitting Earth in 2032By Robert Lea
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Cheng Soon Ong

"... probability probably does not exist — but it is often useful to act as if it does."
David Spiegelhalter provides a short essay that touches on the main aspects of the elusive idea of probability.
⁠nature.com/articles/d41586-024

His book on Uncertainty just came out yesterday, which I expect will explain these ideas in more detail.
⁠penguin.com.au/books/the-art-o

www.nature.comWhy probability probably doesn’t exist (but it is useful to act like it does)All of statistics and much of science depends on probability — an astonishing achievement, considering no one’s really sure what it is.
#MachineLearning#Statistics#Probability
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Eric Maugendre

"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) stat.columbia.edu/~gelman/stuf

#modeling#nullHypothesis#probability
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