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Norster Egg:
Don't worry little guy, the helmet is spoon proof.
#mbt3d #3D #b3d #blender #modelling #blender3d #hardsurface #mastoart #egg #Easter
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#modelling
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A little low down shot of the Norster Egg:
#mbt3d #3D #b3d #blender #modelling #blender3d #hardsurface #mastoart #viking #Easter #egg
Don't mess with the Norsemen Eggs:
I just had to make a Norster Egg, why should the East get all the fun 
#mbt3d #3D #b3d #blender #modelling #blender3d #hardsurface #mastoart #Easter #egg #viking
Useless Mug model:
#mbt3d #3D #b3d #blender #modelling #blender3d #hardsurface #mastoart #bowl #mug
Meshing Bowl V
Another little wood and metal variation of the Blender bowl model:
#mbt3d #3D #b3d #blender #modelling #blender3d #hardsurface #mastoart #bowl
#PortfolioDay once again here are some of the stuff I made this year:
#mbt3d #3D #b3d #blender #modelling #blender3d #hardsurface #mastoart #Portfolio
https://www.europesays.com/1970994/ We’ve all talked about potential economic consequences for Australia of Trump’s policies. Now they’re happening #AnthonyAlbanese #australia #DonaldTrump #Economy #election #FederalElection #GinaRinehart #InterestRateCuts #LauraTingle #Modelling #PeterDutton #ReciprocalTariffs #ReserveBank #TrumpTariffs
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 
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.