Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Multi-scale Modeling of Factor Matrix without using Partition
  Tree   The multi-scale factor models are particularly appealing for analyzing
matrix- or tensor-valued data, due to their adaptiveness to local geometry and
intuitive interpretation. However, the reliance on the binary tree for
recursive partitioning creates high complexity in the parameter space, making
it extremely challenging to quantify its uncertainty. In this article, we
discover an alternative way to generate multi-scale matrix using simple matrix
operation: starting from a random matrix with each column having two unique
values, its Cholesky whitening transform obeys a recursive partitioning
structure. This allows us to consider a generative distribution with large
prior support on common multi-scale factor models, and efficient posterior
computation via Hamiltonian Monte Carlo. We demonstrate its potential in a
multi-scale factor model to find broader regions of interest for human brain
connectivity.

15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an