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mschauer / ZigZagBoomerang.jl

Sleek implementations of the ZigZag, Boomerang and other assorted piecewise deterministic Markov processes for Markov Chain Monte Carlo including Sticky PDMPs for variable selection
MIT License
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reversible version #43

Closed mschauer closed 3 years ago

codecov-io commented 3 years ago

Codecov Report

Merging #43 (092a140) into master (45cd729) will increase coverage by 0.49%. The diff coverage is 75.00%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master      #43      +/-   ##
==========================================
+ Coverage   60.44%   60.93%   +0.49%     
==========================================
  Files          15       15              
  Lines         766      768       +2     
==========================================
+ Hits          463      468       +5     
+ Misses        303      300       -3
Impacted Files Coverage Δ
src/ss_fact.jl 88.88% <75.00%> (+3.03%) :arrow_up:

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mschauer commented 3 years ago

Is it that we don't use the function 

function ssmove_forward!(t, x, θ, t′, Z::Union{BouncyParticle, ZigZag})

at all?

SebaGraz commented 3 years ago

Is it that we don't use the function

function ssmove_forward!(t, x, θ, t′, Z::Union{BouncyParticle, ZigZag})

at all?

Yes, we don't use this function at all. This is because for the sticky ZigZag we implemented directly the sparse algorithm, skipping the non-sparse version.