2,170 research outputs found
A comparative study of multiple-criteria decision-making methods under stochastic inputs
This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative
Bioengineering models of cell signaling
Strategies for rationally manipulating cell behavior in cell-based technologies and molecular therapeutics and understanding effects of environmental agents on physiological systems may be derived from a mechanistic understanding of underlying signaling mechanisms that regulate cell functions. Three crucial attributes of signal transduction necessitate modeling approaches for analyzing these systems: an ever-expanding plethora of signaling molecules and interactions, a highly interconnected biochemical scheme, and concurrent biophysical regulation. Because signal flow is tightly regulated with positive and negative feedbacks and is bidirectional with commands traveling both from outside-in and inside-out, dynamic models that couple biophysical and biochemical elements are required to consider information processing both during transient and steady-state conditions. Unique mathematical frameworks will be needed to obtain an integrated perspective on these complex systems, which operate over wide length and time scales. These may involve a two-level hierarchical approach wherein the overall signaling network is modeled in terms of effective "circuit" or "algorithm" modules, and then each module is correspondingly modeled with more detailed incorporation of its actual underlying biochemical/biophysical molecular interactions
Efficient Sampling and Structure Learning of Bayesian Networks
Bayesian networks are probabilistic graphical models widely employed to
understand dependencies in high dimensional data, and even to facilitate causal
discovery. Learning the underlying network structure, which is encoded as a
directed acyclic graph (DAG) is highly challenging mainly due to the vast
number of possible networks. Efforts have focussed on two fronts:
constraint-based methods that perform conditional independence tests to exclude
edges and score and search approaches which explore the DAG space with greedy
or MCMC schemes. Here we synthesise these two fields in a novel hybrid method
which reduces the complexity of MCMC approaches to that of a constraint-based
method. Individual steps in the MCMC scheme only require simple table lookups
so that very long chains can be efficiently obtained. Furthermore, the scheme
includes an iterative procedure to correct for errors from the conditional
independence tests. The algorithm offers markedly superior performance to
alternatives, particularly because DAGs can also be sampled from the posterior
distribution, enabling full Bayesian model averaging for much larger Bayesian
networks.Comment: Revised version. 40 pages including 16 pages of supplement, 5 figures
and 15 supplemental figures; R package BiDAG is available at
https://CRAN.R-project.org/package=BiDA
Simple Regret Optimization in Online Planning for Markov Decision Processes
We consider online planning in Markov decision processes (MDPs). In online
planning, the agent focuses on its current state only, deliberates about the
set of possible policies from that state onwards and, when interrupted, uses
the outcome of that exploratory deliberation to choose what action to perform
next. The performance of algorithms for online planning is assessed in terms of
simple regret, which is the agent's expected performance loss when the chosen
action, rather than an optimal one, is followed.
To date, state-of-the-art algorithms for online planning in general MDPs are
either best effort, or guarantee only polynomial-rate reduction of simple
regret over time. Here we introduce a new Monte-Carlo tree search algorithm,
BRUE, that guarantees exponential-rate reduction of simple regret and error
probability. This algorithm is based on a simple yet non-standard state-space
sampling scheme, MCTS2e, in which different parts of each sample are dedicated
to different exploratory objectives. Our empirical evaluation shows that BRUE
not only provides superior performance guarantees, but is also very effective
in practice and favorably compares to state-of-the-art. We then extend BRUE
with a variant of "learning by forgetting." The resulting set of algorithms,
BRUE(alpha), generalizes BRUE, improves the exponential factor in the upper
bound on its reduction rate, and exhibits even more attractive empirical
performance
Sensitivity of inferences in forensic genetics to assumptions about founding genes
Many forensic genetics problems can be handled using structured systems of
discrete variables, for which Bayesian networks offer an appealing practical
modeling framework, and allow inferences to be computed by probability
propagation methods. However, when standard assumptions are violated--for
example, when allele frequencies are unknown, there is identity by descent or
the population is heterogeneous--dependence is generated among founding genes,
that makes exact calculation of conditional probabilities by propagation
methods less straightforward. Here we illustrate different methodologies for
assessing sensitivity to assumptions about founders in forensic genetics
problems. These include constrained steepest descent, linear fractional
programming and representing dependence by structure. We illustrate these
methods on several forensic genetics examples involving criminal
identification, simple and complex disputed paternity and DNA mixtures.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS235 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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