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#modelling

12 Beiträge9 Beteiligte0 Beiträge heute
Öffentlich
in silico Plants

SIMPLACE - A versatile #modelling and simulation framework for sustainable crops and agroecosystems
📰 Story: botany.one/2023/06/simplace-th via @botanyone
🔬 Research: doi.org/pfrv from Andreas Enders, Murilo Vianna, Thomas Gaiser, Gunther Krauss, Heidi Webber, Amit Kumar Srivastava, Sabine Julia Seidel, Andreas Tewes, Ehsan Eyshi Rezaei, Frank Ewert
#PlantScience

Öffentlich
in silico Plants

SIMPLACE - A versatile #modelling and simulation framework for sustainable crops and agroecosystems
📰 Story: botany.one/2023/06/simplace-th via @botanyone
🔬 Research: doi.org/10.1093/insilicoplants from Andreas Enders, Murilo Vianna, Thomas Gaiser, Gunther Krauss, Heidi Webber, Amit Kumar Srivastava, Sabine Julia Seidel, Andreas Tewes, Ehsan Eyshi Rezaei, Frank Ewert
#PlantScience

Öffentlich
Burkhard Maria Zimmermann

'The impact of #AI has been felt across industries from #Hollywood to publishing – and now it’s come for #modelling.

#H&M announced last week that it would create AI “twins” of 30 #models with the intention of using them in social media posts and marketing imagery if the model gives her permission.'

theguardian.com/fashion/2025/m

The Guardian · Calling all fashion models … now AI is coming for youVon Lauren Cochrane
Öffentlich
Joris Meys

While reading this great paper by @djnavarro I learnt a new term: mathematistry (Box, 1976). It's using formal tools to define a statistical problem that differs from the scientific one, solving the redefined problem, and then declaring the scientific concern addressed.

The rest of the article was a great read and insight. That term was a little slap in the face that a lot of statisticians should get once in a while 🙂

#statistics #modelling #review #Science

link.springer.com/article/10.1

SpringerLinkBetween the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection - Computational Brain & BehaviorDiscussions of model selection in the psychological literature typically frame the issues as a question of statistical inference, with the goal being to determine which model makes the best predictions about data. Within this setting, advocates of leave-one-out cross-validation and Bayes factors disagree on precisely which prediction problem model selection questions should aim to answer. In this comment, I discuss some of these issues from a scientific perspective. What goal does model selection serve when all models are known to be systematically wrong? How might “toy problems” tell a misleading story? How does the scientific goal of explanation align with (or differ from) traditional statistical concerns? I do not offer answers to these questions, but hope to highlight the reasons why psychological researchers cannot avoid asking them.
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