Penalized Estimation of Directed Acyclic Graphs From Discrete Data

Bayesian networks, with structure given by a directed acyclic graph (DAG), are a popular class of graphical models. However, learning Bayesian networks from discrete or categorical data is particularly challenging, due to the large parameter space and the difficulty in searching for a sparse structure. In this article, we develop a maximum penalized likelihood method to tackle this problem. Instead of the commonly used multinomial distribution, we model the conditional distribution of a node given its parents by multi-logit regression, in which an edge is parameterized by a set of coefficient vectors with dummy variables encoding the levels of a node. To obtain a sparse DAG, a group norm penalty is employed, and a blockwise coordinate descent algorithm is developed to maximize the penalized likelihood subject to the acyclicity constraint of a DAG. When interventional data are available, our method constructs a causal network, in which a directed edge represents a causal relation. We apply our method to various simulated and real data sets. The results show that our method is very competitive, compared to many existing methods, in DAG estimation from both interventional and high-dimensional observational data.Comment: To appear in Statistics and Computin

Mott-Hubbard transition in infinite dimensions

We analyze the unanalytical structure of metal-insulator transition (MIT) in infinite dimensions. By introducing a simple transformation into the dynamical mean-field equation of Hubbard model, a multiple-valued structure in Green's function and other thermodynamical quantities with respect to the interaction strength $U$ are found at low temperatures. A unified description of stable, metastable and unstable phases is obtained in the regime $U_{c1}(T)<U<U_{c2}(T)$, and the Maxwell construction is performed to evaluate the MIT line $U^{\ast}(T)$. We show how the first-order MIT at $U^{\ast}(T)$ for $T>0$ evolves into second-order one at $U_{c2}(0)$ for $T=0$. The phase diagram near MIT is presented.Comment: 5 pages with 3 figures, text and figures revise

Disorder effect of resonant spin Hall effect in a tilted magnetic field

We study the disorder effect of resonant spin Hall effect in a two-dimension electron system with Rashba coupling in the presence of a tilted magnetic field. The competition between the Rashba coupling and the Zeeman coupling leads to the energy crossing of the Landau levels, which gives rise to the resonant spin Hall effect. Utilizing the Streda's formula within the self-consistent Born approximation, we find that the impurity scattering broadens the energy levels, and the resonant spin Hall conductance exhibits a double peak around the resonant point, which is recovered in an applied titled magnetic field.Comment: 6 pages, 4 figure