Equations of States in Statistical Learning for a Nonparametrizable and Regular Case

Many learning machines that have hierarchical structure or hidden variables are now being used in information science, artificial intelligence, and bioinformatics. However, several learning machines used in such fields are not regular but singular statistical models, hence their generalization performance is still left unknown. To overcome these problems, in the previous papers, we proved new equations in statistical learning, by which we can estimate the Bayes generalization loss from the Bayes training loss and the functional variance, on the condition that the true distribution is a singularity contained in a learning machine. In this paper, we prove that the same equations hold even if a true distribution is not contained in a parametric model. Also we prove that, the proposed equations in a regular case are asymptotically equivalent to the Takeuchi information criterion. Therefore, the proposed equations are always applicable without any condition on the unknown true distribution

Hole dynamics in spin and orbital ordered vanadium perovskites

Hole dynamics in spin and orbital ordered vanadates with perovskite structure is investigated. A mobile hole coupled to the spin excitation (magnon) in the spin G-type and orbital C-type (SG/OC) ordered phase, and that to the orbital excitation (orbiton) in the spin C-type and orbital G-type (SC/OG) one are formulated on an equal footing. The observed fragile character of the (SG/OC) order is attributed to the orbiton softening caused by a reduction of the taggered magnetic order parameter. It is proposed that the qualitatively different hole dynamics in the two spin-orbital ordered phases in vanadates can be probed by the optical spectra.Comment: 4pages, 4figure

Orbital Ordering and Resonant X-ray Scattering in Layered Manganites

In layered manganites with orbital and charge orderings, the degeneracy of the Mn $4p$ orbitals as well as the $3d$ ones is lifted by the effects of the $4p$ bands and the local Coulomb interactions. We formulate the atomic scattering factor for the resonant x-ray scattering in the memory function method by taking into account these effects on an equal footing. It is shown that the polarization dependences of the scattering intensities at the orbital and charge superlattice reflections observed in LaSr$_{2}$Mn$_2$O$_7$ are caused by the local and itinerant characters of $4p$ electrons, respectively. We examine the type of the orbital ordered state.Comment: 4 pages, 3 figure

Theory of Anomalous X-ray Scattering in Orbital Ordered Manganites

We study theoretically the anomalous X-ray scattering as a new probe to observe the orbital orderings and excitations in perovskite manganites. The scattering matrix is given by the virtual electronic excitations from Mn $1s$ level to unoccupied Mn $4p$ level. We find that orbital dependence of the Coulomb interaction between Mn $3d$ and Mn $4p$ electrons is essential to bring about the anisotropy of the scattering factor near the K edge. The calculated results in $MnO_6$ clusters explain the forbidden reflections observed in $La_{0.5}Sr_{1.5}MnO_4$ and $LaMnO_3$. A possibility of the observation of the orbital waves by the X-ray scattering is discussed.Comment: 4 pages, 2 figure

A Widely Applicable Bayesian Information Criterion

A statistical model or a learning machine is called regular if the map taking a parameter to a probability distribution is one-to-one and if its Fisher information matrix is always positive definite. If otherwise, it is called singular. In regular statistical models, the Bayes free energy, which is defined by the minus logarithm of Bayes marginal likelihood, can be asymptotically approximated by the Schwarz Bayes information criterion (BIC), whereas in singular models such approximation does not hold. Recently, it was proved that the Bayes free energy of a singular model is asymptotically given by a generalized formula using a birational invariant, the real log canonical threshold (RLCT), instead of half the number of parameters in BIC. Theoretical values of RLCTs in several statistical models are now being discovered based on algebraic geometrical methodology. However, it has been difficult to estimate the Bayes free energy using only training samples, because an RLCT depends on an unknown true distribution. In the present paper, we define a widely applicable Bayesian information criterion (WBIC) by the average log likelihood function over the posterior distribution with the inverse temperature $1/\log n$, where $n$ is the number of training samples. We mathematically prove that WBIC has the same asymptotic expansion as the Bayes free energy, even if a statistical model is singular for and unrealizable by a statistical model. Since WBIC can be numerically calculated without any information about a true distribution, it is a generalized version of BIC onto singular statistical models.Comment: 30 page