8,737 research outputs found
A Linear Programming Approach to Sequential Hypothesis Testing
Under some mild Markov assumptions it is shown that the problem of designing
optimal sequential tests for two simple hypotheses can be formulated as a
linear program. The result is derived by investigating the Lagrangian dual of
the sequential testing problem, which is an unconstrained optimal stopping
problem, depending on two unknown Lagrangian multipliers. It is shown that the
derivative of the optimal cost function with respect to these multipliers
coincides with the error probabilities of the corresponding sequential test.
This property is used to formulate an optimization problem that is jointly
linear in the cost function and the Lagrangian multipliers and an be solved for
both with off-the-shelf algorithms. To illustrate the procedure, optimal
sequential tests for Gaussian random sequences with different dependency
structures are derived, including the Gaussian AR(1) process.Comment: 25 pages, 4 figures, accepted for publication in Sequential Analysi
When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks
We introduce a framework for the modeling of sequential data capturing
pathways of varying lengths observed in a network. Such data are important,
e.g., when studying click streams in information networks, travel patterns in
transportation systems, information cascades in social networks, biological
pathways or time-stamped social interactions. While it is common to apply graph
analytics and network analysis to such data, recent works have shown that
temporal correlations can invalidate the results of such methods. This raises a
fundamental question: when is a network abstraction of sequential data
justified? Addressing this open question, we propose a framework which combines
Markov chains of multiple, higher orders into a multi-layer graphical model
that captures temporal correlations in pathways at multiple length scales
simultaneously. We develop a model selection technique to infer the optimal
number of layers of such a model and show that it outperforms previously used
Markov order detection techniques. An application to eight real-world data sets
on pathways and temporal networks shows that it allows to infer graphical
models which capture both topological and temporal characteristics of such
data. Our work highlights fallacies of network abstractions and provides a
principled answer to the open question when they are justified. Generalizing
network representations to multi-order graphical models, it opens perspectives
for new data mining and knowledge discovery algorithms.Comment: 10 pages, 4 figures, 1 table, companion python package pathpy
available on gitHu
Distinguishing Hidden Markov Chains
Hidden Markov Chains (HMCs) are commonly used mathematical models of
probabilistic systems. They are employed in various fields such as speech
recognition, signal processing, and biological sequence analysis. We consider
the problem of distinguishing two given HMCs based on an observation sequence
that one of the HMCs generates. More precisely, given two HMCs and an
observation sequence, a distinguishing algorithm is expected to identify the
HMC that generates the observation sequence. Two HMCs are called
distinguishable if for every there is a distinguishing
algorithm whose error probability is less than . We show that one
can decide in polynomial time whether two HMCs are distinguishable. Further, we
present and analyze two distinguishing algorithms for distinguishable HMCs. The
first algorithm makes a decision after processing a fixed number of
observations, and it exhibits two-sided error. The second algorithm processes
an unbounded number of observations, but the algorithm has only one-sided
error. The error probability, for both algorithms, decays exponentially with
the number of processed observations. We also provide an algorithm for
distinguishing multiple HMCs. Finally, we discuss an application in stochastic
runtime verification.Comment: This is the full version of a LICS'16 pape
Characterization of Model-Based Detectors for CPS Sensor Faults/Attacks
A vector-valued model-based cumulative sum (CUSUM) procedure is proposed for
identifying faulty/falsified sensor measurements. First, given the system
dynamics, we derive tools for tuning the CUSUM procedure in the fault/attack
free case to fulfill a desired detection performance (in terms of false alarm
rate). We use the widely-used chi-squared fault/attack detection procedure as a
benchmark to compare the performance of the CUSUM. In particular, we
characterize the state degradation that a class of attacks can induce to the
system while enforcing that the detectors (CUSUM and chi-squared) do not raise
alarms. In doing so, we find the upper bound of state degradation that is
possible by an undetected attacker. We quantify the advantage of using a
dynamic detector (CUSUM), which leverages the history of the state, over a
static detector (chi-squared) which uses a single measurement at a time.
Simulations of a chemical reactor with heat exchanger are presented to
illustrate the performance of our tools.Comment: Submitted to IEEE Transactions on Control Systems Technolog
Detecting simultaneous variant intervals in aligned sequences
Given a set of aligned sequences of independent noisy observations, we are
concerned with detecting intervals where the mean values of the observations
change simultaneously in a subset of the sequences. The intervals of changed
means are typically short relative to the length of the sequences, the subset
where the change occurs, the "carriers," can be relatively small, and the sizes
of the changes can vary from one sequence to another. This problem is motivated
by the scientific problem of detecting inherited copy number variants in
aligned DNA samples. We suggest a statistic based on the assumption that for
any given interval of changed means there is a given fraction of samples that
carry the change. We derive an analytic approximation for the false positive
error probability of a scan, which is shown by simulations to be reasonably
accurate. We show that the new method usually improves on methods that analyze
a single sample at a time and on our earlier multi-sample method, which is most
efficient when the carriers form a large fraction of the set of sequences. The
proposed procedure is also shown to be robust with respect to the assumed
fraction of carriers of the changes.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS400 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
An Information-Theoretic Test for Dependence with an Application to the Temporal Structure of Stock Returns
Information theory provides ideas for conceptualising information and
measuring relationships between objects. It has found wide application in the
sciences, but economics and finance have made surprisingly little use of it. We
show that time series data can usefully be studied as information -- by noting
the relationship between statistical redundancy and dependence, we are able to
use the results of information theory to construct a test for joint dependence
of random variables. The test is in the same spirit of those developed by
Ryabko and Astola (2005, 2006b,a), but differs from these in that we add extra
randomness to the original stochatic process. It uses data compression to
estimate the entropy rate of a stochastic process, which allows it to measure
dependence among sets of random variables, as opposed to the existing
econometric literature that uses entropy and finds itself restricted to
pairwise tests of dependence. We show how serial dependence may be detected in
S&P500 and PSI20 stock returns over different sample periods and frequencies.
We apply the test to synthetic data to judge its ability to recover known
temporal dependence structures.Comment: 22 pages, 7 figure
Marginal likelihoods in phylogenetics: a review of methods and applications
By providing a framework of accounting for the shared ancestry inherent to
all life, phylogenetics is becoming the statistical foundation of biology. The
importance of model choice continues to grow as phylogenetic models continue to
increase in complexity to better capture micro and macroevolutionary processes.
In a Bayesian framework, the marginal likelihood is how data update our prior
beliefs about models, which gives us an intuitive measure of comparing model
fit that is grounded in probability theory. Given the rapid increase in the
number and complexity of phylogenetic models, methods for approximating
marginal likelihoods are increasingly important. Here we try to provide an
intuitive description of marginal likelihoods and why they are important in
Bayesian model testing. We also categorize and review methods for estimating
marginal likelihoods of phylogenetic models, highlighting several recent
methods that provide well-behaved estimates. Furthermore, we review some
empirical studies that demonstrate how marginal likelihoods can be used to
learn about models of evolution from biological data. We discuss promising
alternatives that can complement marginal likelihoods for Bayesian model
choice, including posterior-predictive methods. Using simulations, we find one
alternative method based on approximate-Bayesian computation (ABC) to be
biased. We conclude by discussing the challenges of Bayesian model choice and
future directions that promise to improve the approximation of marginal
likelihoods and Bayesian phylogenetics as a whole.Comment: 33 pages, 3 figure
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