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51 results for “sethaxen”
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@sethaxen Depends a bit. Do you want to draw them or generate from data?#pgfplots is simplying that a lot and one can define custom styles to not require yourself to copy paste a lot of code. That's also the case when using #TikZ directly. #TeXLaTeX Depending on the structure there might be packages to support you. https://ctan.org/topic/pgf-tikz
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🚨 New #JuliaLang package! StanLogDensityProblems.jl is a really basic package that implements the LogDensityProblems.jl interface for @mcmc_stan models, built on BridgeStan.jl. It also integrates with PosteriorDB.jl, which makes it really easy to benchmark a new inference method against a large number of models. #ProbProg #MCMCStan
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New package! MRCFile.jl is a #JuliaLang parser for the MRC file format commonly used to store data like that produced by electron microscopy. #StructBio https://github.com/sethaxen/MRCFile.jl
I wrote this package for a project during my PhD and just now got around to registering it. I don't work with MRC files anymore, so if you do and are interested in co-maintaining, let me know!
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Soon Turing.jl users will be able to natively store all sampling outputs in an @ArviZ InferenceData object.
To experiment with the bleeding edge, check out https://github.com/sethaxen/DynamicPPLInferenceObjects.jl!
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With PosteriorDB.jl v0.3.0, it's easier than ever to load #Stan models from posteriordb for sampling with StanSample.jl.
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I just migrated from @[email protected] to this new account at fosstodon.org, so time for a reintroduction!
I'm a #MachineLearning engineer with a focus on probabilistic programming (#probprog) at @unituebingen, where I help scientists use ML for their research. In the office and out, one of my main passions is #FOSS, and I work on a number of #opensource packages, mostly in #JuliaLang :julia: with a focus on #probprog, #manifolds, and #autodiff.
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Workshops, lectures, conferences - acedemics travel a lot. But how do you get to scientific events #climatefriendly? By bike, by train or not travelling at all?
Check out our latest blog article with a video interview!
#opinionpiece #businesstravel
@unituebingen @CellTypist @sethaxen @chrigihttps://www.machinelearningforscience.de/en/how-to-make-business-trips-more-climate-friendly/
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The real vectorization vec(⋅) stacks the input columns into a vector. The Kronecker product ⊗ is related by vec(ABC) = (Cᵀ ⊗ B) vec(B).
We can similarly define a complex version vecc(⋅) = [vec(Re(⋅)); vec(Im(⋅))], with a corresponding #Kronecker product kroncc(⋅,⋅) such that vecc(ABC) = kroncc(Cᵀ, B) vecc(B).
Does anyone know of any literature that discusses the relevant properties of vecc and kroncc? They naturally appear when computing #Jacobians of functions of complex matrices.
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The real vectorization vec(⋅) stacks the input columns into a vector. The Kronecker product ⊗ is related by vec(ABC) = (Cᵀ ⊗ B) vec(B).
We can similarly define a complex version vecc(⋅) = [vec(Re(⋅)); vec(Im(⋅))], with a corresponding #Kronecker product kroncc(⋅,⋅) such that vecc(ABC) = kroncc(Cᵀ, B) vecc(B).
Does anyone know of any literature that discusses the relevant properties of vecc and kroncc? They naturally appear when computing #Jacobians of functions of complex matrices.
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The real vectorization vec(⋅) stacks the input columns into a vector. The Kronecker product ⊗ is related by vec(ABC) = (Cᵀ ⊗ B) vec(B).
We can similarly define a complex version vecc(⋅) = [vec(Re(⋅)); vec(Im(⋅))], with a corresponding #Kronecker product kroncc(⋅,⋅) such that vecc(ABC) = kroncc(Cᵀ, B) vecc(B).
Does anyone know of any literature that discusses the relevant properties of vecc and kroncc? They naturally appear when computing #Jacobians of functions of complex matrices.
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The real vectorization vec(⋅) stacks the input columns into a vector. The Kronecker product ⊗ is related by vec(ABC) = (Cᵀ ⊗ B) vec(B).
We can similarly define a complex version vecc(⋅) = [vec(Re(⋅)); vec(Im(⋅))], with a corresponding #Kronecker product kroncc(⋅,⋅) such that vecc(ABC) = kroncc(Cᵀ, B) vecc(B).
Does anyone know of any literature that discusses the relevant properties of vecc and kroncc? They naturally appear when computing #Jacobians of functions of complex matrices.
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The real vectorization vec(⋅) stacks the input columns into a vector. The Kronecker product ⊗ is related by vec(ABC) = (Cᵀ ⊗ B) vec(B).
We can similarly define a complex version vecc(⋅) = [vec(Re(⋅)); vec(Im(⋅))], with a corresponding #Kronecker product kroncc(⋅,⋅) such that vecc(ABC) = kroncc(Cᵀ, B) vecc(B).
Does anyone know of any literature that discusses the relevant properties of vecc and kroncc? They naturally appear when computing #Jacobians of functions of complex matrices.
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Recently I've gotten really excited about #Enzyme as the future of #autodiff in #JuliaLang, in particular because it supports more language features than #Zygote (e.g. mutation, fast control flow, and preserving structural sparsity). I've started getting acquainted with its rules system, and I have some first impressions by comparison to #ChainRules. 🧵
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@ArviZ @TuringLang Also, in the next few weeks @ArviZ will have pure #JuliaLang implementations of all core functionality *except* for #Bayesian plots! We have a number of #GSoC #JSoC projects for porting plotting recipes in Plots.jl and Makie.jl. If you like visualizing posterior draws; love clean, informative plots; and enjoy one or both of these plotting packages, this project could be a great fit for you!
See e.g. https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#model-diagnostics-plots-julia for details!
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@ArviZ @TuringLang Also, in the next few weeks @ArviZ will have pure #JuliaLang implementations of all core functionality *except* for #Bayesian plots! We have a number of #GSoC #JSoC projects for porting plotting recipes in Plots.jl and Makie.jl. If you like visualizing posterior draws; love clean, informative plots; and enjoy one or both of these plotting packages, this project could be a great fit for you!
See e.g. https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#model-diagnostics-plots-julia for details!
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@ArviZ @TuringLang Also, in the next few weeks @ArviZ will have pure #JuliaLang implementations of all core functionality *except* for #Bayesian plots! We have a number of #GSoC #JSoC projects for porting plotting recipes in Plots.jl and Makie.jl. If you like visualizing posterior draws; love clean, informative plots; and enjoy one or both of these plotting packages, this project could be a great fit for you!
See e.g. https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#model-diagnostics-plots-julia for details!
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@ArviZ @TuringLang Also, in the next few weeks @ArviZ will have pure #JuliaLang implementations of all core functionality *except* for #Bayesian plots! We have a number of #GSoC #JSoC projects for porting plotting recipes in Plots.jl and Makie.jl. If you like visualizing posterior draws; love clean, informative plots; and enjoy one or both of these plotting packages, this project could be a great fit for you!
See e.g. https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#model-diagnostics-plots-julia for details!
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@ArviZ @TuringLang Also, in the next few weeks @ArviZ will have pure #JuliaLang implementations of all core functionality *except* for #Bayesian plots! We have a number of #GSoC #JSoC projects for porting plotting recipes in Plots.jl and Makie.jl. If you like visualizing posterior draws; love clean, informative plots; and enjoy one or both of these plotting packages, this project could be a great fit for you!
See e.g. https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#model-diagnostics-plots-julia for details!
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Reminder: I'll be mentoring a joint @ArviZ and @TuringLang #GSoC #JSoC project working on model-refitting for leave-one-out cross-validation. This project will make model comparison and assessment in Turing.jl more ergonomic and lay key groundwork for similar integration with other PPLs. If that sounds like something you'd like to work on, let us know! #JuliaLang
For details, see https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#reloo-for-turing-models-julia. Please boost to help reach potential applicants!
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Reminder: I'll be mentoring a joint @ArviZ and @TuringLang #GSoC #JSoC project working on model-refitting for leave-one-out cross-validation. This project will make model comparison and assessment in Turing.jl more ergonomic and lay key groundwork for similar integration with other PPLs. If that sounds like something you'd like to work on, let us know! #JuliaLang
For details, see https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#reloo-for-turing-models-julia. Please boost to help reach potential applicants!
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Reminder: I'll be mentoring a joint @ArviZ and @TuringLang #GSoC #JSoC project working on model-refitting for leave-one-out cross-validation. This project will make model comparison and assessment in Turing.jl more ergonomic and lay key groundwork for similar integration with other PPLs. If that sounds like something you'd like to work on, let us know! #JuliaLang
For details, see https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#reloo-for-turing-models-julia. Please boost to help reach potential applicants!
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Reminder: I'll be mentoring a joint @ArviZ and @TuringLang #GSoC #JSoC project working on model-refitting for leave-one-out cross-validation. This project will make model comparison and assessment in Turing.jl more ergonomic and lay key groundwork for similar integration with other PPLs. If that sounds like something you'd like to work on, let us know! #JuliaLang
For details, see https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#reloo-for-turing-models-julia. Please boost to help reach potential applicants!
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Reminder: I'll be mentoring a joint @ArviZ and @TuringLang #GSoC #JSoC project working on model-refitting for leave-one-out cross-validation. This project will make model comparison and assessment in Turing.jl more ergonomic and lay key groundwork for similar integration with other PPLs. If that sounds like something you'd like to work on, let us know! #JuliaLang
For details, see https://github.com/arviz-devs/arviz/wiki/GSoC-2023-projects#reloo-for-turing-models-julia. Please boost to help reach potential applicants!
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While walking around the Tübingen old town, I ran into some folks I met at #BayesComp2023 in Finland just 2 weeks ago. They're in town for the #ProbNumSchool starting tomorrow. Looking forward to it!
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Were #BayesComp2023 talks recorded? And if so, will attendees be able to access them? Really curious about some of the talks in sessions I didn't see.
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Some of my favorite photos from Levi, Finland. #BayesComp2023
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Finally met a bunch of fellow @ArviZ devs at #BayesComp2023! @avehtari @junpenglao
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#BayesComp2023 is over! This has been by far my favorite conference experience. The location was beautiful, the talks were all excellent, and I met so many people in person I'd heard of or interacted with online. Many thanks to the organizers! I'm already looking forward to 2025!
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On Thursday I skied the 2nd time, and this was much more pleasant. It was clear and sunny, and I found a way to the blue slopes on the south side. It turns out to get better one really needs more than 3 seconds to practice technique before ending up face first in the snow! By the time I reached the bottom I learned how to parallel turn. I feel like I'm ready for a red slope now, but that will need to wait for another trip. #BayesComp2023
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@junpenglao @avehtari @mcmc_stan @pymc @TuringLang While I loved all the panelists' answers, in answer to the question, "how will probabilistic programming evolve in the future?", I'd say let's do better at automating what can be automated. IMO users shouldn't have to think about vectorizing their models, marginalizing out discrete parameters, or reparameterizing to improve geometry. This takes valuable time away from the real work of thinking about the question, model, and data. #ProbProg