Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

Stochastic models and simulation of ion channel dynamics

The behaviour of ion channels within cardiac and neuronal cells is intrinsically stochastic in nature. When the number of channels is small this stochastic noise is large and can have an impact on the dynamics of the system which is potentially an issue when modelling small neurons and drug block in cardiac cells. While exact methods correctly capture the stochastic dynamics of a system they are computationally expensive, restricting their inclusion into tissue level models and so approximations to exact methods are often used instead. The other issue in modelling ion channel dynamics is that the transition rates are voltage dependent, adding a level of complexity as the channel dynamics are coupled to the membrane potential. By assuming that such transition rates are constant over each time step, it is possible to derive a stochastic differential equation (SDE), in the same manner as for biochemical reaction networks, that describes the stochastic dynamics of ion channels. While such a model is more computationally efficient than exact methods we show that there are analytical problems with the resulting SDE as well as issues in using current numerical schemes to solve such an equation. We therefore make two contributions: develop a different model to describe the stochastic ion channel dynamics that analytically behaves in the correct manner and also discuss numerical methods that preserve the analytical properties of the model.

Computer modeling of diffusion in biological tissues

Molecular-level computer simulations of restricted water diffusion can be used to develop models for relating diffusion tensor imaging measurements of anisotropic tissue to microstructural tissue characteristics. The diffusion tensors resulting from these simulations can then be analyzed in terms of their relationship to the structural anisotropy of the model used. As the translational motion of water molecules is essentially random, their dynamics can be effectively simulated using computers. In addition to modeling water dynamics and water-tissue interactions, the simulation software of the present study was developed to automatically generate collagen fiber networks from user-defined parameters. This flexibility provides the opportunity for further investigations of the relationship between the diffusion tensor of water and morphologically different models representing different anisotropic tissues.

Nanostructured metal hydrides for hydrogen storage studied by in situ synchrotron and neutron diffraction

This work was focused on studies of the metal hydride materials having a potential in building hydrogen storage systems with high gravimetric and volumetric efficiencies of H storage and formed / decomposed with high rates of hydrogen exchange. In situ diffraction studies of the metal-hydrogen systems were explored as a valuable tool in probing both the mechanism of the phase-structural transformations and their kinetics. Two complementary techniques, namely Neutron Powder Diffraction (NPD) and Synchrotron X-ray diffraction (SR XRD) were utilised. High pressure in situ NPD studies were performed at D2 pressures reaching 1000 bar at the D1B diffractometer accommodated at Institute Laue Langevin, Grenoble. The data of the time resolved in situ SR XRD were collected at the Swiss Norwegian Beam Lines, ESRF, Grenoble in the pressure range up to 50 bar H2 at temperatures 20-400°C. The systems studied by NPD at high pressures included deuterated Al-modified Laves-type C15 ZrFe2-xAlx intermetallics with x = 0.02; 0.04 and 0.20 and the CeNi5-D2 system. D content, hysteresis of H uptake and release, unit cell expansion and stability of the hydrides systematically change with Al content. Deuteration exhibited a very fast kinetics; it resulted in increase of the unit cells volumes reaching 23.5 % for ZrFe1.98Al0.02D2.9(1) and associated with exclusive occupancy of the Zr2(Fe,Al)2 tetrahedra. For CeNi5 deuteration yielded a hexahydride CeNi5D6.2 (20°C, 776 bar D2) and was accompanied by a nearly isotropic volume expansion reaching 30.1% (∆a/a=10.0%; ∆c/c=7.5%). Deuterium atoms fill three different interstitial sites including Ce2Ni2, Ce2Ni3 and Ni4. Significant hysteresis was observed on the first absorption-desorption cycle. This hysteresis decreased on the absorption-desorption cycling. A different approach to the development of H storage systems is based on the hydrides of light elements, first of all the Mg-based ones. These systems were studied by SR XRD. Reactive ball milling in hydrogen (HRBM) allowed synthesis of the nanostructured Mg-based hydrides. The experimental parameters (PH2, T, energy of milling, ball / sample ratio and balls size), significantly influence rate of hydrogenation. The studies confirmed (a) a completeness of hydrogenation of Mg into MgH2; (b) indicated a partial transformation of the originally formed -MgH2 into a metastable -MgH2 (a ratio / was 3/1); (c) yielded the crystallite size for the main hydrogenation product, -MgH2, as close to 10 nm. Influence of the additives to Mg on the structure and hydrogen absorption/desorption properties and cycle behaviour of the composites was established and will be discussed in the paper.

Relation between charge carrier mobility and lifetime in organic photovoltaics

The relationship between charge carrier lifetime and mobility in a bulk heterojunction based organic solar cell, utilizing diketopyrrolopyrole- naphthalene co-polymer and PC71BM in the photoactive blend layer, is investigated using the photoinduced charge extraction by linearly increasing voltage technique. Light intensity, delay time, and temperature dependent experiments are used to quantify the charge carrier mobility and density as well as the temperature dependence of both. From the saturation of photoinduced current at high laser intensities, it is shown that Langevin-type bimolecular recombination is present in the studied system. The charge carrier lifetime, especially in Langevin systems, is discussed to be an ambiguous and unreliable parameter to determine the performance of organic solar cells, because of the dependence of charge carrier lifetime on charge carrier density, mobility, and type of recombination. It is revealed that the relation between charge mobility (μ) and lifetime (τ) is inversely proportional, where the μτ product is independent of temperature. The results indicate that in photovoltaic systems with Langevin type bimolecular recombination, the strategies to increase the charge lifetime might not be beneficial because of an accompanying reduction in charge carrier mobility. Instead, the focus on non-Langevin mechanisms of recombination is crucial, because this allows an increase in the charge extraction rate by improving the carrier lifetime, density, and mobility simultaneously. © 2013 AIP Publishing LLC.

A stochastic exponential Euler scheme for simulation of stiff biochemical reaction systems

In order to simulate stiff biochemical reaction systems, an explicit exponential Euler scheme is derived for multidimensional, non-commutative stochastic differential equations with a semilinear drift term. The scheme is of strong order one half and A-stable in mean square. The combination with this and the projection method shows good performance in numerical experiments dealing with an alternative formulation of the chemical Langevin equation for a human ether a-go-go related gene ion channel mode

Fluctuation phenomena in electrochemistry part I. The formalism

A unified approach to the problem of electrochemical fluctuations is presented. On the basis of the Langevin procedure, primary noise sources are introduced in the basic phenomenological equations and a discussion of the secondary noise sources arising in the expressions for the power spectra of currents is given.

The fully implicit stochastic-alpha method for stiff stochastic differential equations

A fully implicit integration method for stochastic differential equations with significant multiplicative noise and stiffness in both the drift and diffusion coefficients has been constructed, analyzed and illustrated with numerical examples in this work. The method has strong order 1.0 consistency and has user-selectable parameters that allow the user to expand the stability region of the method to cover almost the entire drift-diffusion stability plane. The large stability region enables the method to take computationally efficient time steps. A system of chemical Langevin equations simulated with the method illustrates its computational efficiency.

A Model of Anomalous Chain Translocation Dynamics

A model of polymer translocation based on the stochastic dynamics of the number of monomers on one side of a pore-containing surface is formulated in terms of a one-dimensional generalized Langevin equation, in which the random force is assumed to be characterized by long-ranged temporal correlations. The model is introduced to rationalize anomalies in measured and simulated values of the average time of passage through the pore, which in general cannot be satisfactorily accounted for by simple Brownian diffusion mechanisms. Calculations are presented of the mean first passage time for barrier crossing and of the mean square displacement of a monomeric segment, in the limits of strong and weak diffusive bias. The calculations produce estimates of the exponents in various scaling relations that are in satisfactory agreement with available data.

Polymer melt dynamics: Microscopic roots of fractional viscoelasticity

The rheological properties of polymer melts and other complex macromolecular fluids are often successfully modeled by phenomenological constitutive equations containing fractional differential operators. We suggest a molecular basis for such fractional equations in terms of the generalized Langevin equation (GLE) that underlies the renormalized Rouse model developed by Schweizer [J. Chem. Phys. 91, 5802 (1989)]. The GLE describes the dynamics of the segments of a tagged chain under the action of random forces originating in the fast fluctuations of the surrounding polymer matrix. By representing these random forces as fractional Gaussian noise, and transforming the GLE into an equivalent diffusion equation for the density of the tagged chain segments, we obtain an analytical expression for the dynamic shear relaxation modulus G(t), which we then show decays as a power law in time. This power-law relaxation is the root of fractional viscoelastic behavior.

Observation of a power-law memory kernel for fluctuations within a single protein molecule

The fluctuation of the distance between a fluorescein-tyrosine pair within a single protein complex was directly monitored in real time by photoinduced electron transfer and found to be a stationary, time-reversible, and non-Markovian Gaussian process. Within the generalized Langevin equation formalism, we experimentally determine the memory kernel K(t), which is proportional to the autocorrelation function of the random fluctuating force. K(t) is a power-law decay, t(-0.51 +/- 0.07) in a broad range of time scales (10(-3)-10 s). Such a long-time memory effect could have implications for protein functions.

Hysteresis in model spin systems

The authors study the hysteretic response of model spin systems to periodic time-varying fields H(t) as a function of the amplitude H0 and the frequency Omega . At fixed H0, they find conventional, squarish hysteresis loops at low Omega , and rounded, roughly elliptical loops at high Omega , in agreement with experiment. For the O(N to infinity ), d=3, ( Phi 2)2 model with Langevin dynamics, they find a novel scaling behaviour for the area A of the hysteresis loop, of the form (valid for low fields) A approximately=H0066 Omega 0.33.

Rigorous scaling law for the heat current in disordered harmonic chain

We study the energy current in a model of heat conduction, first considered in detail by Casher and Lebowitz. The model consists of a one-dimensional disordered harmonic chain of n i.i.d. random masses, connected to their nearest neighbors via identical springs, and coupled at the boundaries to Langevin heat baths, with respective temperatures T_1 and T_n. Let EJ_n be the steady-state energy current across the chain, averaged over the masses. We prove that EJ_n \sim (T_1 - T_n)n^{-3/2} in the limit n \to \infty, as has been conjectured by various authors over the time. The proof relies on a new explicit representation for the elements of the product of associated transfer matrices.

Temperature dependent free energy surface of polymer folding from equilibrium and quench studies

Langevin dynamics simulation studies have been employed to calculate the temperature dependent free energy surface and folding characteristics of a 500 monomer long linear alkane (polyethylene) chain with a realistic interaction potential. Both equilibrium and temperature quench simulation studies have been carried out. Using the shape anisotropy parameter (S) of the folded molecule as the order parameter, we find a weakly first order phase transition between the high-temperature molten globule and low-temperature rodlike crystalline states separated by a small barrier of the order of k(B)T. Near the melting temperature (580 K), we observe an intriguing intermittent fluctuation with pronounced ``1/f noise characteristics'' between these two states with large difference in shape and structure. We have also studied the possibilities of different pathways of folding to states much below the melting point. At 300 K starting from the all-trans linear configuration, the chain folds stepwise into a very regular fourfold crystallite with very high shape anisotropy. Whereas, when quenched from a high temperature (900 K) random coil regime, we identify a two step transition from the random coiled state to a molten globulelike state and, further, to a anisotropic rodlike state. The trajectory reveals an interesting coupling between the two order parameters, namely, radius of gyration (R-g) and the shape anisotropy parameter (S). The rodlike final state of the quench trajectory is characterized by lower shape anisotropy parameter and significantly larger number of gauche defects as compared to the final state obtained through equilibrium simulation starting from all-trans linear chain. The quench study shows indication of a nucleationlike pathway from the molten globule to the rodlike state involving an underlying rugged energy landscape. (C) 2010 American Institute of Physics. doi:10.1063/1.3509398]