Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting paleoclimate from compositional data using multivariate
  Gaussian process inverse prediction   Multivariate compositional count data arise in many applications including
ecology, microbiology, genetics, and paleoclimate. A frequent question in the
analysis of multivariate compositional count data is what values of a
covariate(s) give rise to the observed composition. Learning the relationship
between covariates and the compositional count allows for inverse prediction of
unobserved covariates given compositional count observations. Gaussian
processes provide a flexible framework for modeling functional responses with
respect to a covariate without assuming a functional form. Many scientific
disciplines use Gaussian process approximations to improve prediction and make
inference on latent processes and parameters. When prediction is desired on
unobserved covariates given realizations of the response variable, this is
called inverse prediction. Because inverse prediction is mathematically and
computationally challenging, predicting unobserved covariates often requires
fitting models that are different from the hypothesized generative model. We
present a novel computational framework that allows for efficient inverse
prediction using a Gaussian process approximation to generative models. Our
framework enables scientific learning about how the latent processes co-vary
with respect to covariates while simultaneously providing predictions of
missing covariates. The proposed framework is capable of efficiently exploring
the high dimensional, multi-modal latent spaces that arise in the inverse
problem. To demonstrate flexibility, we apply our method in a generalized
linear model framework to predict latent climate states given multivariate
count data. Based on cross-validation, our model has predictive skill
competitive with current methods while simultaneously providing formal,
statistical inference on the underlying community dynamics of the biological
system previously not available.

31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol