575 - Ask the max effort model what two plus two is and watch it deliberate. Right answer, three pages of reasoning. Feels like handing your math homework to an engineer dad.
youtu.be/oB9GQPX_7Fc
#ai #llm #claude #modelselection
youtu.be/oB9GQPX_7Fc
#ai #llm #claude #modelselection
November 5, 2026 at 1:18 PM
575 - Ask the max effort model what two plus two is and watch it deliberate. Right answer, three pages of reasoning. Feels like handing your math homework to an engineer dad.
youtu.be/oB9GQPX_7Fc
#ai #llm #claude #modelselection
youtu.be/oB9GQPX_7Fc
#ai #llm #claude #modelselection
Gemini free users now stuck in Auto mode with new Low/Medium/High effort levels. #Gemini #GoogleAI #FreeTier #ModelSelection #EffortLevels #AIUX https://thedailytechfeed.com/free-gemini-users-now-forced-into-auto-mode-with-new-thinking-levels/
October 10, 2026 at 5:17 AM
Gemini free users now stuck in Auto mode with new Low/Medium/High effort levels. #Gemini #GoogleAI #FreeTier #ModelSelection #EffortLevels #AIUX https://thedailytechfeed.com/free-gemini-users-now-forced-into-auto-mode-with-new-thinking-levels/
What does a model cost across a week of real work—not one call? Use Tempr’s reference pricing to compare recurring workflows, then check context and capabilities. #AIModels #ModelSelection https://temprhq.io/models
October 7, 2026 at 12:11 AM
What does a model cost across a week of real work—not one call? Use Tempr’s reference pricing to compare recurring workflows, then check context and capabilities. #AIModels #ModelSelection https://temprhq.io/models
#statstab #625 {lavinteract}: Post-Estimation Utilities for 'lavaan' Fitted Models
Thoughts: "Companion toolbox for structural equation models fitted with 'lavaan'"
#lavaan #sem #modelselection #modelfit #r2 #FDR #VIF #latent #rstats #r
cran.r-project.org/web/packages...
Thoughts: "Companion toolbox for structural equation models fitted with 'lavaan'"
#lavaan #sem #modelselection #modelfit #r2 #FDR #VIF #latent #rstats #r
cran.r-project.org/web/packages...
Help for package lavinteract
cran.r-project.org
September 25, 2026 at 5:41 PM
When should you use an LLM over a statistical model? Three real-world cases reveal how data, representation, and training determine which approach wins. #modelselection
When an LLM Beats a Statistical Model, and When It Doesn't
hackernoon.com
September 6, 2026 at 3:44 PM
When should you use an LLM over a statistical model? Three real-world cases reveal how data, representation, and training determine which approach wins. #modelselection
#statstab #610 Philosophy and the practice of Bayesian statistics
Thoughts: Bayesian as a hypothetico-deductive approach to science.
#bayesian #philosophy #bayes #inference #probability #modelselection #modelchecking #assumptions
sites.stat.columbia.edu/gelman/resea...
Thoughts: Bayesian as a hypothetico-deductive approach to science.
#bayesian #philosophy #bayes #inference #probability #modelselection #modelchecking #assumptions
sites.stat.columbia.edu/gelman/resea...
sites.stat.columbia.edu
September 4, 2026 at 5:01 PM
#statstab #610 Philosophy and the practice of Bayesian statistics
Thoughts: Bayesian as a hypothetico-deductive approach to science.
#bayesian #philosophy #bayes #inference #probability #modelselection #modelchecking #assumptions
sites.stat.columbia.edu/gelman/resea...
Thoughts: Bayesian as a hypothetico-deductive approach to science.
#bayesian #philosophy #bayes #inference #probability #modelselection #modelchecking #assumptions
sites.stat.columbia.edu/gelman/resea...
Model Selection and Model Simplification using R
Choose, compare, and refine models from AIC and BIC to cross-validation and averaging
Instructor: Dr Mark Andrews
9–10 Dec
prstats.org/course/model-selection-and-model-simplification-msms06
#Statistics #Rstats #DataAnalysis #ModelSelection #Bayesian
Choose, compare, and refine models from AIC and BIC to cross-validation and averaging
Instructor: Dr Mark Andrews
9–10 Dec
prstats.org/course/model-selection-and-model-simplification-msms06
#Statistics #Rstats #DataAnalysis #ModelSelection #Bayesian
Model Selection and Model Simplification | PR Statistics
This two-day course offers practical, hands-on training in statistical model selection, comparison, and evaluation using R. Participants will learn to choose among competing models, handle multiple pr...
prstats.org
November 10, 2025 at 11:18 AM
Model Selection and Model Simplification using R
Choose, compare, and refine models from AIC and BIC to cross-validation and averaging
Instructor: Dr Mark Andrews
9–10 Dec
prstats.org/course/model-selection-and-model-simplification-msms06
#Statistics #Rstats #DataAnalysis #ModelSelection #Bayesian
Choose, compare, and refine models from AIC and BIC to cross-validation and averaging
Instructor: Dr Mark Andrews
9–10 Dec
prstats.org/course/model-selection-and-model-simplification-msms06
#Statistics #Rstats #DataAnalysis #ModelSelection #Bayesian
New CRAN package modelSelection with initial version 1.0.3
#rstats
https://cran.r-project.org/package=modelSelection
#rstats
https://cran.r-project.org/package=modelSelection
CRAN: Package modelSelection
Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) <<a href="https://doi.org/10.5281%2Fzenodo.17119597" target="_top">doi:10.5281/zenodo.17119597</a>> (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package.
cran.r-project.org
September 21, 2025 at 2:02 PM
New CRAN package modelSelection with initial version 1.0.3
#rstats
https://cran.r-project.org/package=modelSelection
#rstats
https://cran.r-project.org/package=modelSelection
Time to pick your champion model! Discover the secrets to choosing the best one for your project. #ModelSelection #AI
January 14, 2026 at 3:30 PM
Time to pick your champion model! Discover the secrets to choosing the best one for your project. #ModelSelection #AI
PSISLOOCVFAQ - *cough* - bayesian cross-validation frequently asked questions answered: https://avehtari.github.io/modelselection/CV-FAQ.html#23_What_is_the_relationship_between_AIC,_DIC,_WAIC_and_LOO-CV
Cross-validation FAQ
avehtari.github.io
July 31, 2023 at 2:01 PM
PSISLOOCVFAQ - *cough* - bayesian cross-validation frequently asked questions answered: https://avehtari.github.io/modelselection/CV-FAQ.html#23_What_is_the_relationship_between_AIC,_DIC,_WAIC_and_LOO-CV
Bayesian Regularization for Dynamical System Identification: Additive Noise Models
www.mdpi.com/2673-9984/12...
By Robert K. Niven, Laurent Cordier, Ali Mohammad-Djafari, Markus Abel and Markus Quade
From the MaxEnt 2024 Workshop
#BayesianInference #DynamicalSystems #ModelSelection
www.mdpi.com/2673-9984/12...
By Robert K. Niven, Laurent Cordier, Ali Mohammad-Djafari, Markus Abel and Markus Quade
From the MaxEnt 2024 Workshop
#BayesianInference #DynamicalSystems #ModelSelection
December 23, 2025 at 2:31 PM
Bayesian Regularization for Dynamical System Identification: Additive Noise Models
www.mdpi.com/2673-9984/12...
By Robert K. Niven, Laurent Cordier, Ali Mohammad-Djafari, Markus Abel and Markus Quade
From the MaxEnt 2024 Workshop
#BayesianInference #DynamicalSystems #ModelSelection
www.mdpi.com/2673-9984/12...
By Robert K. Niven, Laurent Cordier, Ali Mohammad-Djafari, Markus Abel and Markus Quade
From the MaxEnt 2024 Workshop
#BayesianInference #DynamicalSystems #ModelSelection
Updates on CRAN: bibliometrix (5.4.0), gridmicrotex (0.0.2), longitree (1.0.1), MAIHDA (0.1.8), MIRDD (0.2.4), misty (0.8.2), modelSelection (1.0.7), qcapower (0.2.0), reproducible (3.1.0)
May 16, 2026 at 5:55 PM
Updates on CRAN: bibliometrix (5.4.0), gridmicrotex (0.0.2), longitree (1.0.1), MAIHDA (0.1.8), MIRDD (0.2.4), misty (0.8.2), modelSelection (1.0.7), qcapower (0.2.0), reproducible (3.1.0)
Updates on CRAN: mlexperiments (1.0.0), mllrnrs (0.0.8), mlr3oml (0.12.0), mlr3summary (0.1.1), mlr3torch (0.3.3), mlsurvlrnrs (0.0.8), mmap (0.6-24), mnormt (2.1.2), modelSelection (1.0.5), modeltime (1.3.5), modsem (1.0.16), MonteCarloSEM (2.0.0), MR.RGM (0.1.0)
February 3, 2026 at 3:37 AM
Updates on CRAN: mlexperiments (1.0.0), mllrnrs (0.0.8), mlr3oml (0.12.0), mlr3summary (0.1.1), mlr3torch (0.3.3), mlsurvlrnrs (0.0.8), mmap (0.6-24), mnormt (2.1.2), modelSelection (1.0.5), modeltime (1.3.5), modsem (1.0.16), MonteCarloSEM (2.0.0), MR.RGM (0.1.0)
Updates on CRAN: atime (2025.9.30), cmsafvis (1.3.0), DominoDataR (0.3.0), inDAGO (1.0.3), mets (1.3.8), MLwrap (0.2.1), modelSelection (1.0.4), R2MLwiN (0.8-10), svglite (2.2.2), VSURF (1.2.1)
October 21, 2025 at 5:21 PM
Updates on CRAN: atime (2025.9.30), cmsafvis (1.3.0), DominoDataR (0.3.0), inDAGO (1.0.3), mets (1.3.8), MLwrap (0.2.1), modelSelection (1.0.4), R2MLwiN (0.8-10), svglite (2.2.2), VSURF (1.2.1)
#statstab #548 Checking model assumption {easystats}
Thoughts: The {performance} package is great at a one-function plot for assunptions. Good explanations also (bug theory limited).
#rstats #assumptions #linearity #linearmodel #r #modelselection
easystats.github.io/performance/...
Thoughts: The {performance} package is great at a one-function plot for assunptions. Good explanations also (bug theory limited).
#rstats #assumptions #linearity #linearmodel #r #modelselection
easystats.github.io/performance/...
June 9, 2026 at 7:32 PM
#statstab #548 Checking model assumption {easystats}
Thoughts: The {performance} package is great at a one-function plot for assunptions. Good explanations also (bug theory limited).
#rstats #assumptions #linearity #linearmodel #r #modelselection
easystats.github.io/performance/...
Thoughts: The {performance} package is great at a one-function plot for assunptions. Good explanations also (bug theory limited).
#rstats #assumptions #linearity #linearmodel #r #modelselection
easystats.github.io/performance/...
#statstab #467 Hypothesis testing, model selection, model comparison some thoughts
Thoughts: An excellent (but too short) discussion on bayesian inference.
#bayesian #bayesfactor #modelselection #inference #NBHT #BF #ROPE #primer
discourse.mc-stan.org/t/hypothesis...
Thoughts: An excellent (but too short) discussion on bayesian inference.
#bayesian #bayesfactor #modelselection #inference #NBHT #BF #ROPE #primer
discourse.mc-stan.org/t/hypothesis...
Hypothesis testing, model selection, model comparison - some thoughts
EDIT: This was an attempt to write guidance. It turns out I stepped quite far from my depth and the text sounded much more conclusive than it should. I think it is correct to currently just classify i...
discourse.mc-stan.org
November 25, 2025 at 6:58 PM
#statstab #467 Hypothesis testing, model selection, model comparison some thoughts
Thoughts: An excellent (but too short) discussion on bayesian inference.
#bayesian #bayesfactor #modelselection #inference #NBHT #BF #ROPE #primer
discourse.mc-stan.org/t/hypothesis...
Thoughts: An excellent (but too short) discussion on bayesian inference.
#bayesian #bayesfactor #modelselection #inference #NBHT #BF #ROPE #primer
discourse.mc-stan.org/t/hypothesis...
#statstab #393 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements [actual post]
Thoughts: #392 has the comments, but this is where the magic happens.
#modelselection #modelcomparison #variance #effectsize #tutorial
www.fharrell.com/post/addvalue/
Thoughts: #392 has the comments, but this is where the magic happens.
#modelselection #modelcomparison #variance #effectsize #tutorial
www.fharrell.com/post/addvalue/
Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements – Statistical Thinking
Researchers have used contorted, inefficient, and arbitrary analyses to demonstrated added value in biomarkers, genes, and new lab measurements. Traditional statistical measures have always been up to...
www.fharrell.com
July 23, 2025 at 6:48 PM
#statstab #393 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements [actual post]
Thoughts: #392 has the comments, but this is where the magic happens.
#modelselection #modelcomparison #variance #effectsize #tutorial
www.fharrell.com/post/addvalue/
Thoughts: #392 has the comments, but this is where the magic happens.
#modelselection #modelcomparison #variance #effectsize #tutorial
www.fharrell.com/post/addvalue/
#statstab #392 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements
Thoughts: Forums can be great for asking the author for exact answers to complex questions
#modelselection #causalinference #prediction #bias #information
discourse.datamethods.org/t/statistica...
Thoughts: Forums can be great for asking the author for exact answers to complex questions
#modelselection #causalinference #prediction #bias #information
discourse.datamethods.org/t/statistica...
Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements
This topic is for discussions about Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements
discourse.datamethods.org
July 22, 2025 at 8:24 PM
#statstab #392 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements
Thoughts: Forums can be great for asking the author for exact answers to complex questions
#modelselection #causalinference #prediction #bias #information
discourse.datamethods.org/t/statistica...
Thoughts: Forums can be great for asking the author for exact answers to complex questions
#modelselection #causalinference #prediction #bias #information
discourse.datamethods.org/t/statistica...
#statstab #358 What are some of the problems with stepwise regression?
Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.
#stepwise #regression #QRPs #issues #phacking #modelselection #bias
www.stata.com/support/faqs...
Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.
#stepwise #regression #QRPs #issues #phacking #modelselection #bias
www.stata.com/support/faqs...
Stata | FAQ: Problems with stepwise regression
What are some of the problems with stepwise regression?
www.stata.com
June 4, 2025 at 2:48 PM
#statstab #358 What are some of the problems with stepwise regression?
Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.
#stepwise #regression #QRPs #issues #phacking #modelselection #bias
www.stata.com/support/faqs...
Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.
#stepwise #regression #QRPs #issues #phacking #modelselection #bias
www.stata.com/support/faqs...
Picking the right LLM isn’t about topping charts—it’s about solving your actual problems. Dive into why real‑world needs beat benchmark bragging rights. #RealWorldAI #ModelSelection #LLMPerformance
🔗 aidailypost.com/news/choosin...
🔗 aidailypost.com/news/choosin...
June 4, 2026 at 9:51 PM
Picking the right LLM isn’t about topping charts—it’s about solving your actual problems. Dive into why real‑world needs beat benchmark bragging rights. #RealWorldAI #ModelSelection #LLMPerformance
🔗 aidailypost.com/news/choosin...
🔗 aidailypost.com/news/choosin...
Turns out a 90% router accuracy beats bigger models for AI orchestration. Learn why precise routing and specialist LLMs matter more than sheer size. #AIRouterAccuracy #SemanticRouting #ModelSelection
🔗 aidailypost.com/news/ai-orch...
🔗 aidailypost.com/news/ai-orch...
February 3, 2026 at 5:43 PM
Turns out a 90% router accuracy beats bigger models for AI orchestration. Learn why precise routing and specialist LLMs matter more than sheer size. #AIRouterAccuracy #SemanticRouting #ModelSelection
🔗 aidailypost.com/news/ai-orch...
🔗 aidailypost.com/news/ai-orch...