Search fresh public links, source activity, and ready-to-use post angles for Statistical Ml.
Fresh curated links around statistical ML are collected here so marketers can spot useful updates and turn timely ideas into posts faster.
Recent items include:
Recent curated links from global sources. Generate one free draft from any story, then use SocialBu to schedule and refine your content calendar.
A model is almost never 100% sure of anything. These 10 probability concepts explain how it makes decisions anyway.
Supervised LearningContinue reading on Medium »
In Supervised learning, we often indirectly optimize the outcome by seeing how well the machine learning model scores on the training data…Continue reading on Medium »
We’ve talked about uncertainty in polls (see Margin of Error, Total Margin of Error, Total Margin of Error II) and we’ve talked about ranked data (see exploded logit !). A new pape...
Regularization Frameworks: Taming Financial Market NoiseContinue reading on Medium »
Due tomorrow (June 10): Enter a contest for Alexandre Andorra’s interview of Aki, Richard, and Andrew about their new book Bayesian Workflow. I hope folks ask about evaluating MRP...
by David J. Warne, Xiangrun Zhu, Thomas P. Steele, Stuart T. Johnston, Scott A. Sisson, Matthew Faria, Ryan J. Murphy, Alexander P. Browning Biological systems exhibit substantial...
Sample size calculation is one of those things that feels solved. You open a textbook, pick a formula, plug in your numbers, and move on…Continue reading on Medium »
Using artificial intelligence to decide where the next Euro goes.Continue reading on Berlin Tech Blog (by mobile.de & Kleinanzeigen) »
Meng (2022) pops up a lot here: “it is the people” (the launch of this blog series a year ago !), “probability samples vs epsem samples vs SRS samples”, “divine probabilities”, and...
Continue reading on Medium »
Ryan Giordano, Alice Cima, Jared Murray, Erin Hartman, and Avi Feller write: Multilevel regression and poststratification (MrP) has become a workhorse method for estimating populat...
Financial time-series models often fail for a boring reason: the feature matrix is noisier than the model can use.Continue reading on Medium »
Representation, Evaluation, and Optimization are the main 3 building blocks when it comes to making a great machine learning model.Continue reading on Medium »
Любой аналитик знает, что самым надёжным способом проверки гипотез являются рандомизированные контролируемые эксперименты (RCT), или, как их называют в народе — A/B-тесты. На практ...
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings The post Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model appeared first on Toward...
The above sketch shows a decision tree. The circles are uncertainty nodes and the squares are decision nodes. Read the tree from left to right: to start, there is uncertainty of wh...
Want to understand LLMs better? Start with these five foundational papers that explain how they work.
Want to understand LLMs better? Start with these five foundational papers that explain how they work.
This post is from Bob. I’ve been thinking a lot about scores (gradients of the log density function) and how they can be used for convergence monitoring. We know that the expected...
At Databricks, we’ve built a unique inference platform that serves every frontier...
TL;DR: Descriptive vs. Inferential Statistics compares two key approaches: descriptive statistics summarize and present data (mean, median, charts), while inferential statistics us...
How unlearning fixes mode collapse in synthetic survey replies The post Can LLMs Replace Survey Respondents? appeared first on Towards Data Science.
Use SocialBu to discover ideas, generate post drafts, and schedule them across your social channels.