AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung and Chang, 1989; Shachter and Peot, 1989) and self-importance sampling (Shachter and Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, and Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, and Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm

Nonperturbative model for optical response under intense periodic fields with application to graphene in a strong perpendicular magnetic field

Graphene exhibits extremely strong optical nonlinearity when a strong perpendicular magnetic field is applied, the response current shows strong field dependence even for moderate light intensity, and the perturbation theory fails. We nonperturbatively calculate full optical conductivities induced by a periodic field in an equation-of-motion framework based on the Floquet theorem, with the scattering described phenomenologically. The nonlinear response at high fields is understood in terms of the dressed electronic states, or Floquet states, which is further characterized by the optical conductivity for a weak probe light field. This approach is illustrated for a magnetic field at $5$ T and a driving field with photon energy $0.05$ eV. Our results show that the perturbation theory works only for weak fields $<3$ kV/cm, confirming the extremely strong light matter interaction for Landau levels of graphene. This approach can be easily extended to the calculation of optical conductivities in other systems

Analytical smoothing effect of solution for the boussinesq equations

In this paper, we study the analytical smoothing effect of Cauchy problem for the incompressible Boussinesq equations. Precisely, we use the Fourier method to prove that the Sobolev H 1-solution to the incompressible Boussinesq equations in periodic domain is analytic for any positive time. So the incompressible Boussinesq equation admet exactly same smoothing effect properties of incompressible Navier-Stokes equations

Nonlinear magneto-optic effects in doped graphene and gapped graphene: a perturbative treatment

The nonlinear magneto-optic responses are investigated for gapped graphene and doped graphene in a perpendicular magnetic field. The electronic states are described by Landau levels, and the electron dynamics in an optical field is obtained by solving the density matrix in the equation of motion. In the linear dispersion approximation around the Dirac points, both linear conductivity and third order nonlinear conductivities are numerically evaluated for infrared frequencies. The nonlinear phenomena, including third harmonic generation, Kerr effects and two photon absorption, and four wave mixing, are studied. All optical conductivities show strong dependence on the magnetic field. At weak magnetic fields, our results for doped graphene agree with those in the literature. We also present the spectra of the conductivities of gapped graphene. At strong magnetic fields, the third order conductivities show peaks with varying the magnetic field and the photon energy. These peaks are induced by the resonant transitions between different Landau levels. The resonant channels, the positions, and the divergences of peaks are analyzed. The conductivities can be greatly modified, up to orders of magnitude. The dependence of the conductivities on the gap parameter and the chemical potential is studied.Comment: 18 pages, 8 figure

Calculation of Radiative Corrections to E1 matrix elements in the Neutral Alkalis

Radiative corrections to E1 matrix elements for ns-np transitions in the alkali metal atoms lithium through francium are evaluated. They are found to be small for the lighter alkalis but significantly larger for the heavier alkalis, and in the case of cesium much larger than the experimental accuracy. The relation of the matrix element calculation to a recent decay rate calculation for hydrogenic ions is discussed, and application of the method to parity nonconservation in cesium is described

Haar expectations of ratios of random characteristic polynomials

We compute Haar ensemble averages of ratios of random characteristic polynomials for the classical Lie groups K = O(N), SO(N), and USp(N). To that end, we start from the Clifford-Weyl algebera in its canonical realization on the complex of holomorphic differential forms for a C-vector space V. From it we construct the Fock representation of an orthosymplectic Lie superalgebra osp associated to V. Particular attention is paid to defining Howe's oscillator semigroup and the representation that partially exponentiates the Lie algebra representation of sp in osp. In the process, by pushing the semigroup representation to its boundary and arguing by continuity, we provide a construction of the Shale-Weil-Segal representation of the metaplectic group. To deal with a product of n ratios of characteristic polynomials, we let V = C^n \otimes C^N where C^N is equipped with its standard K-representation, and focus on the subspace of K-equivariant forms. By Howe duality, this is a highest-weight irreducible representation of the centralizer g of Lie(K) in osp. We identify the K-Haar expectation of n ratios with the character of this g-representation, which we show to be uniquely determined by analyticity, Weyl group invariance, certain weight constraints and a system of differential equations coming from the Laplace-Casimir invariants of g. We find an explicit solution to the problem posed by all these conditions. In this way we prove that the said Haar expectations are expressed by a Weyl-type character formula for all integers N \ge 1. This completes earlier work by Conrey, Farmer, and Zirnbauer for the case of U(N).Comment: LaTeX, 70 pages, Complex Analysis and its Synergies (2016) 2:

Measuring and interpreting current permanent and transitory earnings and dividends : methods and applications / BEBR No. 815

Bibliography: p. 21-22

Equivalence of weak and strong modes of measures on topological vector spaces

A strong mode of a probability measure on a normed space $X$ can be defined as a point $u$ such that the mass of the ball centred at $u$ uniformly dominates the mass of all other balls in the small-radius limit. Helin and Burger weakened this definition by considering only pairwise comparisons with balls whose centres differ by vectors in a dense, proper linear subspace $E$ of $X$, and posed the question of when these two types of modes coincide. We show that, in a more general setting of metrisable vector spaces equipped with measures that are finite on bounded sets, the density of $E$ and a uniformity condition suffice for the equivalence of these two types of modes. We accomplish this by introducing a new, intermediate type of mode. We also show that these modes can be inequivalent if the uniformity condition fails. Our results shed light on the relationships between among various notions of maximum a posteriori estimator in non-parametric Bayesian inference.Comment: 22 pages, 3 figure