#arviz — Public Fediverse posts

Talks:

Olli Ruokojoki: #Data Driven #Batteries: Modeling State of Charge and Health from Operational Data

Tuomas Sivula: Stochastic Models in #EnergyStorage Operating Environments

Quentin Salomé: ⚡️ lightning talk (title TBA)

Osvaldo Martin: ⚡️ #ArviZ 1.0: Refactoring for flexibility, extensibility, and power

Jouni Seppänen: #Python Visualisation Tools

#EnergyTransition #PyData #Helsinki #Meetup #DataScience

Mohács, 1956. Sadly, floods along the Danube are nothing new. The one in March 1956 was particular bad as it was caused by the sudden melting of ice from an especially cold winter. The ice dams that formed had to be bombed by the authorities. Mohács in southern Hungary was badly impacted, as we can see in this photo of some of those displaced by the flooding receiving a meal in a local hotel.

Source: Fortepan [129818] / Antal Kotnyék

#fortepan #Mohács #Hungary #flooding #Danube #árvíz

Mohács, 1956. Sadly, floods along the Danube are nothing new. The one in March 1956 was particular bad as it was caused by the sudden melting of ice from an especially cold winter. The ice dams that formed had to be bombed by the authorities. Mohács in southern Hungary was badly impacted, as we can see in this photo of some of those displaced by the flooding receiving a meal in a local hotel.

Source: Fortepan [129818] / Antal Kotnyék

#fortepan #Mohács #Hungary #flooding #Danube #árvíz

#árvíz #budapest

#árvíz #budapest

I genuinely miss PyMC2. The #PyMC and #Arviz APIs changes so frequently, that it's impossible to know what the standard approach to anything is.

#Bayesian #Statistics in #Python should be easy.

To be honest, I'd really like a well maintained #SkLearn module for it.

I genuinely miss PyMC2. The #PyMC and #Arviz APIs changes so frequently, that it's impossible to know what the standard approach to anything is.

#Bayesian #Statistics in #Python should be easy.

To be honest, I'd really like a well maintained #SkLearn module for it.

I genuinely miss PyMC2. The #PyMC and #Arviz APIs changes so frequently, that it's impossible to know what the standard approach to anything is.

#Bayesian #Statistics in #Python should be easy.

To be honest, I'd really like a well maintained #SkLearn module for it.

I genuinely miss PyMC2. The #PyMC and #Arviz APIs changes so frequently, that it's impossible to know what the standard approach to anything is.

#Bayesian #Statistics in #Python should be easy.

To be honest, I'd really like a well maintained #SkLearn module for it.

I genuinely miss PyMC2. The #PyMC and #Arviz APIs changes so frequently, that it's impossible to know what the standard approach to anything is.

#Bayesian #Statistics in #Python should be easy.

To be honest, I'd really like a well maintained #SkLearn module for it.

Hello Fediverse! This is the official account for the #ArviZ project, providing #FOSS tools for exploratory analysis of #bayesian models!

#introduction #ProbProg #stats #python #JuliaLang

Hello Fediverse! This is the official account for the #ArviZ project, providing #FOSS tools for exploratory analysis of #bayesian models!

#introduction #ProbProg #stats #python #JuliaLang

I spent some time tonight working out a possible major redesign of the InferenceData conversion pipeline in InferenceObjects.jl.

The new design is more expressive and extensible. It also simplifies the API and enables developers to implement a converter for their container of #MCMC draws with much less code.

Feedback appreciated! https://github.com/arviz-devs/InferenceObjects.jl/issues/32

#JuliaLang #ArviZ #Turing #Stan

Finally, part of this effort involves moving this integration code out of #ArviZ.jl, which still has #Python dependencies, and into pure Julia packages, so users get all of this with the convenience of #JuliaLang's package management.

While Python interop in Julia usually works quite well, sometimes the Python environment gets messed up, which blocks users from using ArviZ.jl 😦 , so moving this code to pure Julia packages supports more users.

Things are coming together for #ArviZ's InferenceData (https://github.com/arviz-devs/InferenceObjects.jl) to be a supported output type for #Turing and #JuliaLang's :julia: #Stan interface, similarly to how it is for #PyMC.

For details, see https://github.com/TuringLang/MCMCChains.jl/issues/381 and https://github.com/StanJulia/StanSample.jl/issues/60

#statistics #mcmc_stan #bayesian