this is for holding javascript data
Xavier Holt deleted Slice_Sampling_Slice_sampling_is__.tex
over 8 years ago
Commit id: 62cf9b39f7b42e13bcc9f0bcedf1fdd6ef70127e
deletions | additions
diff --git a/Slice_Sampling_Slice_sampling_is__.tex b/Slice_Sampling_Slice_sampling_is__.tex
deleted file mode 100644
index a5145bf..0000000
--- a/Slice_Sampling_Slice_sampling_is__.tex
+++ /dev/null
...
## Slice Sampling
Slice sampling is a non-deterministic method used to sample from arbitrary curves. Specifically slice sampling belongs to the family of Markov chain Monte-Carlo (McMC) methods. The general principle behind such methods is to construct a Markov chain that has the desired distribution as an equilibrium solution. They are used to obtain numeric approximations from multi-dimensional integrals for which no closed-form solution is readily available. We can then use the samples to construct estimations of statistics on the underlying distribution (mean, mode, variance etc.) by simply calculating the equivalent sample-statistic.
Slice sampling in particular possesses several very desirable properties. It allows us to sample from integrals that are _proportional_ to our underlying distribution. There is no assumption that our probability mass sums to one. Consequently our intractable denominator above is of no concern -- we simply proceed using the scaled posterior.
Additionally, slice sampling has no free parameters. This is useful from a practical perspective, as it removes the need to spend time tuning and updating hyper-parameters for more complex models. It also impacts on performance; the more general Metropolis-Hastings model is highly sensitive to step-size parameters, which means unless we get it right we're going to perform very poorly on evaluation. In contrast, slice sampling adjusts the step-size to match the local shape of the density function.
\cite{murray2009elliptical}
\cite{Tran_2015}
\cite{rennie2005regularized}
diff --git a/layout.md b/layout.md
index 73898ed..ccbfcd4 100644
--- a/layout.md
+++ b/layout.md
...
begin_align_hat_mathbf_w__.tex
Where_our_second_equality_sign__.md
subsection_Prior_for_Weights_In__.tex
Slice_Sampling_Slice_sampling_is__.tex
Since_then_the_use_of__.tex
subsubsection_Input_begin_description_item__.tex