44 research outputs found
Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines
Boltzmann machines (BMs) are a class of binary neural networks for which
there have been numerous proposed methods of estimation. Recently, it has been
shown that in the fully visible case of the BM, the method of maximum
pseudolikelihood estimation (MPLE) results in parameter estimates which are
consistent in the probabilistic sense. In this article, we investigate the
properties of MPLE for the fully visible BMs further, and prove that MPLE also
yields an asymptotically normal parameter estimator. These results can be used
to construct confidence intervals and to test statistical hypotheses. We
support our theoretical results by showing that the estimator behaves as
expected in a simulation study
Financial interaction networks inferred from traded volumes
In order to use the advanced inference techniques available for Ising models,
we transform complex data (real vectors) into binary strings, by local
averaging and thresholding. This transformation introduces parameters, which
must be varied to characterize the behaviour of the system. The approach is
illustrated on financial data, using three inference methods -- equilibrium,
synchronous and asynchronous inference -- to construct functional connections
between stocks. We show that the traded volume information is enough to obtain
well known results about financial markets, which use however the presumably
richer price information: collective behaviour ("market mode") and strong
interactions within industry sectors. Synchronous and asynchronous Ising
inference methods give results which are coherent with equilibrium ones, and
more detailed since the obtained interaction networks are directed.Comment: 14 pages, 6 figure
Exact mean field inference in asymmetric kinetic Ising systems
We develop an elementary mean field approach for fully asymmetric kinetic
Ising models, which can be applied to a single instance of the problem. In the
case of the asymmetric SK model this method gives the exact values of the local
magnetizations and the exact relation between equal-time and time-delayed
correlations. It can also be used to solve efficiently the inverse problem,
i.e. determine the couplings and local fields from a set of patterns, also in
cases where the fields and couplings are time-dependent. This approach
generalizes some recent attempts to solve this dynamical inference problem,
which were valid in the limit of weak coupling. It provides the exact solution
to the problem also in strongly coupled problems. This mean field inference can
also be used as an efficient approximate method to infer the couplings and
fields in problems which are not infinite range, for instance in diluted
asymmetric spin glasses.Comment: 10 pages, 7 figure
Intrinsic limitations of inverse inference in the pairwise Ising spin glass
We analyze the limits inherent to the inverse reconstruction of a pairwise
Ising spin glass based on susceptibility propagation. We establish the
conditions under which the susceptibility propagation algorithm is able to
reconstruct the characteristics of the network given first- and second-order
local observables, evaluate eventual errors due to various types of noise in
the originally observed data, and discuss the scaling of the problem with the
number of degrees of freedom
Beyond inverse Ising model: structure of the analytical solution for a class of inverse problems
I consider the problem of deriving couplings of a statistical model from
measured correlations, a task which generalizes the well-known inverse Ising
problem. After reminding that such problem can be mapped on the one of
expressing the entropy of a system as a function of its corresponding
observables, I show the conditions under which this can be done without
resorting to iterative algorithms. I find that inverse problems are local (the
inverse Fisher information is sparse) whenever the corresponding models have a
factorized form, and the entropy can be split in a sum of small cluster
contributions. I illustrate these ideas through two examples (the Ising model
on a tree and the one-dimensional periodic chain with arbitrary order
interaction) and support the results with numerical simulations. The extension
of these methods to more general scenarios is finally discussed.Comment: 15 pages, 6 figure
Dynamics and Performance of Susceptibility Propagation on Synthetic Data
We study the performance and convergence properties of the Susceptibility
Propagation (SusP) algorithm for solving the Inverse Ising problem. We first
study how the temperature parameter (T) in a Sherrington-Kirkpatrick model
generating the data influences the performance and convergence of the
algorithm. We find that at the high temperature regime (T>4), the algorithm
performs well and its quality is only limited by the quality of the supplied
data. In the low temperature regime (T<4), we find that the algorithm typically
does not converge, yielding diverging values for the couplings. However, we
show that by stopping the algorithm at the right time before divergence becomes
serious, good reconstruction can be achieved down to T~2. We then show that
dense connectivity, loopiness of the connectivity, and high absolute
magnetization all have deteriorating effects on the performance of the
algorithm. When absolute magnetization is high, we show that other methods can
be work better than SusP. Finally, we show that for neural data with high
absolute magnetization, SusP performs less well than TAP inversion.Comment: 9 pages, 7 figure
Dynamic message-passing approach for kinetic spin models with reversible dynamics
A method to approximately close the dynamic cavity equations for synchronous
reversible dynamics on a locally tree-like topology is presented. The method
builds on a graph expansion to eliminate loops from the normalizations of
each step in the dynamics, and an assumption that a set of auxilary
probability distributions on histories of pairs of spins mainly have
dependencies that are local in time. The closure is then effectuated by
projecting these probability distributions on -step Markov processes. The
method is shown in detail on the level of ordinary Markov processes (),
and outlined for higher-order approximations (). Numerical validations of
the technique are provided for the reconstruction of the transient and
equilibrium dynamics of the kinetic Ising model on a random graph with
arbitrary connectivity symmetry.Comment: 6 pages, 4 figure