310 research outputs found
Issues Negotiation™ – investing in stakeholders
Consumers are increasingly demanding and less tolerant of organisations that fail to live up to their expectations. Organisations are expected to change their approach to business, giving the same priority to all stakeholders, with integrity and commitment. This means that the traditional approach to issues management where organisations “decide” on their plans, “dictate” them to stakeholders, and prepare their “defence”, will no longer be adequate. Issues Negotiaion™ offers business leaders a powerful alternative that builds trusting relationships, turning potentially negative issues into competitive advantage. It is a process that supports the organisation in its long-term growth
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
This paper presents a Bayesian generative model for dependent Cox point
processes, alongside an efficient inference scheme which scales as if the point
processes were modelled independently. We can handle missing data naturally,
infer latent structure, and cope with large numbers of observed processes. A
further novel contribution enables the model to work effectively in higher
dimensional spaces. Using this method, we achieve vastly improved predictive
performance on both 2D and 1D real data, validating our structured approach.Comment: Presented at UAI 2014. Bibtex: @inproceedings{structcoxpp14_UAI,
Author = {Tom Gunter and Chris Lloyd and Michael A. Osborne and Stephen J.
Roberts}, Title = {Efficient Bayesian Nonparametric Modelling of Structured
Point Processes}, Booktitle = {Uncertainty in Artificial Intelligence (UAI)},
Year = {2014}
Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a
posteriori estimation of probabilistic program variables. By using a series of
code transformations, the evidence of any probabilistic program, and therefore
of any graphical model, can be optimized with respect to an arbitrary subset of
its sampled variables. To carry out this optimization, we develop the first
Bayesian optimization package to directly exploit the source code of its
target, leading to innovations in problem-independent hyperpriors, unbounded
optimization, and implicit constraint satisfaction; delivering significant
performance improvements over prominent existing packages. We present
applications of our method to a number of tasks including engineering design
and parameter optimization
Recommended from our members
Temporally stable feature clusters for maritime object tracking in visible and thermal imagery
This paper describes a new approach to detect and track maritime objects in real time. The approach particularly addresses the highly dynamic maritime environment, panning cameras, target scale changes, and operates on both visible and thermal imagery. Object detection is based on agglomerative clustering of temporally stable features. Object extents are first determined based on persistence of detected features and their relative separation and motion attributes. An explicit cluster merging and splitting process handles object creation and separation. Stable object clus- ters are tracked frame-to-frame. The effectiveness of the approach is demonstrated on four challenging real-world public datasets
A large conformational change of the translocation ATPase SecA
The ATPase SecA mediates the posttranslational translocation of a wide range of polypeptide substrates through the SecY channel in the cytoplasmic membrane of bacteria. We have determined the crystal structure of a monomeric form of Bacillus subtilis SecA at a 2.2-Ă… resolution. A comparison with the previously determined structures of SecA reveals a nucleotide-independent, large conformational change that opens a deep groove similar to that in other proteins that interact with diverse polypeptides. We propose that the open form of SecA represents an activated state
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic models:
an active learning approach that converges more quickly (in wall-clock time)
than Markov chain Monte Carlo (MCMC) benchmarks. The central challenge in
probabilistic inference is numerical integration, to average over ensembles of
models or unknown (hyper-)parameters (for example to compute the marginal
likelihood or a partition function). MCMC has provided approaches to numerical
integration that deliver state-of-the-art inference, but can suffer from sample
inefficiency and poor convergence diagnostics. Bayesian quadrature techniques
offer a model-based solution to such problems, but their uptake has been
hindered by prohibitive computation costs. We introduce a warped model for
probabilistic integrands (likelihoods) that are known to be non-negative,
permitting a cheap active learning scheme to optimally select sample locations.
Our algorithm is demonstrated to offer faster convergence (in seconds) relative
to simple Monte Carlo and annealed importance sampling on both synthetic and
real-world examples
Recommended from our members
JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator
Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soybean, maize and rice. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soybean at the global and country levels, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index, gross primary production and canopy height better than in the standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an Earth system and crop yield model perspective is encouraging. However, more effort is needed to develop the parametrisation of the model for specific applications. Key future model developments identified include the introduction of processes such as irrigation and nitrogen limitation which will enable better representation of the spatial variability in yield
Rethinking Lifelong Learning within Current Contexts of Time and Space. EcCoWell2 Briefing Paper 7
No abstract available
- …