1,549,157 research outputs found
Combining perturbation theories with halo models
We investigate the building of unified models that can predict the
matter-density power spectrum and the two-point correlation function from very
large to small scales, being consistent with perturbation theory at low and
with halo models at high . We use a Lagrangian framework to re-interpret the
halo model and to decompose the power spectrum into "2-halo" and "1-halo"
contributions, related to "perturbative" and "non-perturbative" terms. We
describe a simple implementation of this model and present a detailed
comparison with numerical simulations, from up to Mpc, and from up to Mpc. We show that the
1-halo contribution contains a counterterm that ensures a tail at low
and is important not to spoil the predictions on the scales probed by baryon
acoustic oscillations, to Mpc. On the other hand,
we show that standard perturbation theory is inadequate for the 2-halo
contribution, because higher order terms grow too fast at high , so that
resummation schemes must be used. We describe a simple implementation, based on
a 1-loop "direct steepest-descent" resummation for the 2-halo contribution that
allows fast numerical computations, and we check that we obtain a good match to
simulations at low and high . Our simple implementation already fares better
than standard 1-loop perturbation theory on large scales and simple fits to the
power spectrum at high , with a typical accuracy of 1% on large scales and
10% on small scales. We obtain similar results for the two-point correlation
function. However, there remains room for improvement on the transition scale
between the 2-halo and 1-halo contributions, which may be the most difficult
regime to describe.Comment: 29 page
Combining forecasts from nested models
Motivated by the common finding that linear autoregressive models forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the unrestricted model is true, but as the sample size grows, the DGP converges to the restricted model. This approach captures the practical reality that the predictive content of variables of interest is often low. We derive MSE-minimizing weights for combining the restricted and unrestricted forecasts. In the Monte Carlo and empirical analysis, we compare the effectiveness of our combination approach against related alternatives, such as Bayesian estimation.Forecasting
Combining forecasts from nested models
Motivated by the common finding that linear autoregressive models often forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the unrestricted model is true, but a subset of the coefficients are treated as being local-to-zero. This approach captures the practical reality that the predictive content of variables of interest is often low. We derive MSE-minimizing weights for combining the restricted and unrestricted forecasts. Monte Carlo and empirical analyses verify the practical e effectiveness of our combination approach.Econometric models ; Economic forecasting
Combining Thesaurus Knowledge and Probabilistic Topic Models
In this paper we present the approach of introducing thesaurus knowledge into
probabilistic topic models. The main idea of the approach is based on the
assumption that the frequencies of semantically related words and phrases,
which are met in the same texts, should be enhanced: this action leads to their
larger contribution into topics found in these texts. We have conducted
experiments with several thesauri and found that for improving topic models, it
is useful to utilize domain-specific knowledge. If a general thesaurus, such as
WordNet, is used, the thesaurus-based improvement of topic models can be
achieved with excluding hyponymy relations in combined topic models.Comment: Accepted to AIST-2017 conference (http://aistconf.ru/). The final
publication will be available at link.springer.co
Combining Models of Approximation with Partial Learning
In Gold's framework of inductive inference, the model of partial learning
requires the learner to output exactly one correct index for the target object
and only the target object infinitely often. Since infinitely many of the
learner's hypotheses may be incorrect, it is not obvious whether a partial
learner can be modifed to "approximate" the target object.
Fulk and Jain (Approximate inference and scientific method. Information and
Computation 114(2):179--191, 1994) introduced a model of approximate learning
of recursive functions. The present work extends their research and solves an
open problem of Fulk and Jain by showing that there is a learner which
approximates and partially identifies every recursive function by outputting a
sequence of hypotheses which, in addition, are also almost all finite variants
of the target function.
The subsequent study is dedicated to the question how these findings
generalise to the learning of r.e. languages from positive data. Here three
variants of approximate learning will be introduced and investigated with
respect to the question whether they can be combined with partial learning.
Following the line of Fulk and Jain's research, further investigations provide
conditions under which partial language learners can eventually output only
finite variants of the target language. The combinabilities of other partial
learning criteria will also be briefly studied.Comment: 28 page
Rho meson properties from combining QCD-based models
Aiming at the calculation of the properties of rho-mesons, non-perturbative
QCD-based methods are discussed concerning their potentials as well as their
short-comings. The latter are overcome by combining these techniques. The
utilized methods are (i) the chiral constituent quark model deduced from the
instanton vacuum model and large-N_c arguments, (ii) chiral perturbation theory
unitarized by the inverse amplitude method and (iii) QCD sum rules. Advantages
of the combination of these methods are especially the absence of un-physical
quark-production thresholds and parameter-free results. Already in the chiral
limit and in leading order in 1/N_c one obtains a reasonable result for the
mass of the rho-meson, namely m_rho = 790 \pm 30 MeV. Using the KSFR relation
the universality of the rho-meson coupling is recovered. The latter is found to
be g = 6.0 \pm 0.3.Comment: 16 pages, 1 figure, Revtex
When topic models disagree: keyphrase extraction with mulitple topic models
We explore how the unsupervised extraction of topic-related keywords benefits from combining multiple topic models. We show that averaging multiple topic models, inferred from different corpora, leads to more accurate keyphrases than when using a single topic model and other state-of-the-art techniques. The experiments confirm the intuitive idea that a prerequisite for the significant benefit of combining multiple models is that the models should be sufficiently different, i.e., they should provide distinct contexts in terms of topical word importance
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