Scalable Inference for Markov Processes with Intractable Likelihoods

Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor. Markov Chain Monte Carlo (MCMC) techniques can lead to exact inference in such models but in practice can suffer performance issues including long burn-in periods and poor mixing. On the other hand approximate Bayesian computation techniques can allow rapid exploration of a large parameter space but yield only approximate posterior distributions. Here we consider the combined use of approximate Bayesian computation (ABC) and MCMC techniques for improved computational efficiency while retaining exact inference on parallel hardware

Anglo-American corporate governance and the employment relationship: a case to answer

The corporate governance environment in the UK and US is generally thought to be hostile to the emergence of cooperative employment relations of the kind exemplified by labour-management partnerships. We discuss case study evidence from the UK which suggests that, contrary to this widespread perception, enduring and proactive partnerships may develop, in conditions where management can convince shareholders of the long-term gains from this approach, and where other regulatory factors operate to extend the time-horizon for financial returns. We conclude that there is more scope than is commonly allowed for measures which could reconcile liquidity in capital markets with cooperation in labour relations

Crystallization of YIoQ, a GTPase of unknown function essential for Bacillus subtilis viability

YLoQ is a putative ATP/GTP-binding protein of unknown function identified from the complete sequence of the Bacillus subtilis genome. A gene-knockout programme established that yloQ is one of a set of some 270 indispensable genes for the viability of this organism. Crystals of YloQ have been grown from HEPES-buffered solutions at pH 7.5 containing polyethylene glycol and diffraction data have been collected extending to 2.5 Angstrom spacing

Modelling the Galactic Magnetic Field on the Plane in 2D

We present a method for parametric modelling of the physical components of the Galaxy's magnetised interstellar medium, simulating the observables, and mapping out the likelihood space using a Markov Chain Monte-Carlo analysis. We then demonstrate it using total and polarised synchrotron emission data as well as rotation measures of extragalactic sources. With these three datasets, we define and study three components of the magnetic field: the large-scale coherent field, the small-scale isotropic random field, and the ordered field. In this first paper, we use only data along the Galactic plane and test a simple 2D logarithmic spiral model for the magnetic field that includes a compression and a shearing of the random component giving rise to an ordered component. We demonstrate with simulations that the method can indeed constrain multiple parameters yielding measures of, for example, the ratios of the magnetic field components. Though subject to uncertainties in thermal and cosmic ray electron densities and depending on our particular model parametrisation, our preliminary analysis shows that the coherent component is a small fraction of the total magnetic field and that an ordered component comparable in strength to the isotropic random component is required to explain the polarisation fraction of synchrotron emission. We outline further work to extend this type of analysis to study the magnetic spiral arm structure, the details of the turbulence as well as the 3D structure of the magnetic field.Comment: 18 pages, 11 figures, updated to published MNRAS versio

Gegenbauer-solvable quantum chain model

In an innovative inverse-problem construction the measured, experimental energies $E_1$, $E_2$, ...$E_N$ of a quantum bound-state system are assumed fitted by an N-plet of zeros of a classical orthogonal polynomial $f_N(E)$. We reconstruct the underlying Hamiltonian $H$ (in the most elementary nearest-neighbor-interaction form) and the underlying Hilbert space ${\cal H}$ of states (the rich menu of non-equivalent inner products is offered). The Gegenbauer's ultraspherical polynomials $f_n(x)=C_n^\alpha(x)$ are chosen for the detailed illustration of technicalities.Comment: 29 pp., 1 fi

Total Quantum Zeno effect and Intelligent States for a two level system in a squeezed bath

In this work we show that by frequent measurements of adequately chosen observables, a complete suppression of the decay in an exponentially decaying two level system interacting with a squeezed bath is obtained. The observables for which the effect is observed depend on the the squeezing parameters of the bath. The initial states which display Total Zeno Effect are intelligent states of two conjugate observables associated to the electromagnetic fluctuations of the bath.Comment: 5 pages, 3 figure

Energy absorption by "sparse" systems: beyond linear response theory

The analysis of the response to driving in the case of weakly chaotic or weakly interacting systems should go beyond linear response theory. Due to the "sparsity" of the perturbation matrix, a resistor network picture of transitions between energy levels is essential. The Kubo formula is modified, replacing the "algebraic" average over the squared matrix elements by a "resistor network" average. Consequently the response becomes semi-linear rather than linear. Some novel results have been obtained in the context of two prototype problems: the heating rate of particles in Billiards with vibrating walls; and the Ohmic Joule conductance of mesoscopic rings driven by electromotive force. Respectively, the obtained results are contrasted with the "Wall formula" and the "Drude formula".Comment: 8 pages, 7 figures, short pedagogical review. Proceedings of FQMT conference (Prague, 2011). Ref correcte