1,413 research outputs found
Efficient sampling for Bayesian inference of conjunctive Bayesian networks
Motivation: Cancer development is driven by the accumulation of advantageous mutations and subsequent clonal expansion of cells harbouring these mutations, but the order in which mutations occur remains poorly understood. Advances in genome sequencing and the soon-arriving flood of cancer genome data produced by large cancer sequencing consortia hold the promise to elucidate cancer progression. However, new computational methods are needed to analyse these large datasets. Results: We present a Bayesian inference scheme for Conjunctive Bayesian Networks, a probabilistic graphical model in which mutations accumulate according to partial order constraints and cancer genotypes are observed subject to measurement noise. We develop an efficient MCMC sampling scheme specifically designed to overcome local optima induced by dependency structures. We demonstrate the performance advantage of our sampler over traditional approaches on simulated data and show the advantages of adopting a Bayesian perspective when reanalyzing cancer datasets and comparing our results to previous maximum-likelihood-based approaches. Availability: An R package including the sampler and examples is available at http://www.cbg.ethz.ch/software/bayes-cbn. Contacts: [email protected]
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science
The Libra Toolkit for Probabilistic Models
The Libra Toolkit is a collection of algorithms for learning and inference
with discrete probabilistic models, including Bayesian networks, Markov
networks, dependency networks, and sum-product networks. Compared to other
toolkits, Libra places a greater emphasis on learning the structure of
tractable models in which exact inference is efficient. It also includes a
variety of algorithms for learning graphical models in which inference is
potentially intractable, and for performing exact and approximate inference.
Libra is released under a 2-clause BSD license to encourage broad use in
academia and industry
Logical Abstractions for Noisy Variational Quantum Algorithm Simulation
Due to the unreliability and limited capacity of existing quantum computer
prototypes, quantum circuit simulation continues to be a vital tool for
validating next generation quantum computers and for studying variational
quantum algorithms, which are among the leading candidates for useful quantum
computation. Existing quantum circuit simulators do not address the common
traits of variational algorithms, namely: 1) their ability to work with noisy
qubits and operations, 2) their repeated execution of the same circuits but
with different parameters, and 3) the fact that they sample from circuit final
wavefunctions to drive a classical optimization routine. We present a quantum
circuit simulation toolchain based on logical abstractions targeted for
simulating variational algorithms. Our proposed toolchain encodes quantum
amplitudes and noise probabilities in a probabilistic graphical model, and it
compiles the circuits to logical formulas that support efficient repeated
simulation of and sampling from quantum circuits for different parameters.
Compared to state-of-the-art state vector and density matrix quantum circuit
simulators, our simulation approach offers greater performance when sampling
from noisy circuits with at least eight to 20 qubits and with around 12
operations on each qubit, making the approach ideal for simulating near-term
variational quantum algorithms. And for simulating noise-free shallow quantum
circuits with 32 qubits, our simulation approach offers a reduction
in sampling cost versus quantum circuit simulation techniques based on tensor
network contraction.Comment: ASPLOS '21, April 19-23, 2021, Virtual, US
Augmenting Deep Learning Performance in an Evidential Multiple Classifier System
International audienceThe main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier's respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision
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