Inverse and Forward Modelling of Shallow-Marine Stratigraphy

This thesis presents the development and application of a numerical inverse and forward model of stratigraphy applied to shallow-marine wave-dominated sedimentary systems. The approach links a “process-based” forward model of stratigraphy (i.e. BARSIM, developed by J.E.A. Storms, University of Delft) to a fully non-linear stochastic inverse scheme. The inverse problem has been formulated using a Bayesian framework in order to sample the full range of uncertainty and explicitly build in prior knowledge. The methodology combines Reversible Jump Markov chain Monte Carlo and Simulated Tempering algorithms which are able to deal with variable dimensional inverse problems and multi-modal posterior probability distributions, respectively. The numerical scheme requires the construction of a likelihood function to quantify the agreement between simulated and observed data (e.g. sediment ages and thicknesses, grain-size distributions). Prior to real case study applications, the method has been successfully validated on different scenarios built from synthetic data, in which the impact of data distribution, quantity and quality on the uncertainty of the inferred environmental parameters were investigated. The numerical scheme has then been applied to two case studies: the outcrop-constrained Lower Cretaceous “Standardville” parasequence of the Aberdeen Member of the Blackhawk Formation (Boock Cliffs, Utah, U.S.A.) and the Emsian sub-surface data of South Algeria. The inverse modelling scheme successfully reproduced stratigraphic architecture in both cases, within the constraints of the input data quality. The inferences of the relative sea level, sediment supply and wave regime histories contribute to the understanding of mechanisms that produced the observed stratigraphy. Of equal importance, the inverse results allowed complete characterisation of uncertainties in these forcing parameters and in the stratigraphic architecture developed in between data constraints. These results suggest that the inverse model may ultimately provide a process-based geological complement to standard geostatistical tools for the static characterization of hydrocarbon reservoirs

The Dynamics of Indonesian Banking Competition 2006 – 2013

There have been many views and hypothesis regarding the impact of competition on banking performances and stability. In order to find the optimum level of competition, we should start by measuring the level of competition in the industry. This article shows the development of competition level in Indonesian banking, measured with four different methods (Concentration ratio, Herfindahl-Hirschmann Index, H-statistic, and Lerner Index). We found that concentration in deposit and loan markets have become slightly more concentrated, with increasing market power indicated by Lerner Index. We also found that Lerner Index of Indonesian banking have a bimodal distribution, which indicates that Indonesian banking tend to be divided into two clusters based on its market power. On the other hand, development of H-statistic illustrates different tendencies where it indicates that banking market power is diminishing. The different result indicates that, even if the overall assets of Indonesian banking have become more productive, it has become more costly for them to earn new assets. Therefore we recommend Indonesian banking to do consolidations in order to gain economies of scale and scope in earning new assets

THE ROLE OF INTEREST RATES AND PROVINCIAL MONETARY AGGREGATE IN MAINTAINING REGIONAL INFLATION IN INDONESIA

In most countries, monetary policies are implemented in order to maintain economic stability. The policy may employ interest rate or money supply to derive the assigned national inflation target. Most studies investigate the relationship between monetary policy and inflation use national data. Based on the idea that inflation is a regional phenomenon, the application of provincial data might be more appropriate explaining the relationship between monetary policy and inflation. The study elaborate the impact of changes in provincial money supply, BI Rate (interest rates of central bank), and PUAB (money market interest rates) to regional inflation in the framework Hybrid New Keynesian Phillips Curve (HNKPC). The study employs Generalized Method of Moments (GMM) techniques on panel data of 32 provinces from 2005-III to 2014-IV. The data is classified into 4 groups, which are Jawa-Bali (W1), Sumatera (W2), Kalimantan-Sulawesi (W3), and Papua-Maluku-Nusa Tenggara (W4). The estimation result shows that provincial monetary aggregate influence inflation significantly only in Sumatera. Furthermore, inflation is also effect by BI Rate in Sumatera and Kalimantan-Sulawesi. The study also found that PUAB is significantly affecting inflation in almost all Indonesian regions, except Kalimantan-Sulawesi. This study concludes that interest rates, BI rate and PUAB, is more appropriate than change in provincial money supply to control provincial inflation.Keywords: monetary policy, regional inflation, hybrid NKPCJEL Classification Numbers: E31, E52, R1

Towards Information Theory-Based Discovery of Equivariances

© 2023 H. Charvin, N. Catenacci Volpi & D. Polani.The presence of symmetries imposes a stringent set of constraints on a system. This constrained structure allows intelligent agents interacting with such a system to drasti- cally improve the efficiency of learning and generalization, through the internalisation of the system’s symmetries into their information-processing. In parallel, principled mod- els of complexity-constrained learning and behaviour make increasing use of information- theoretic methods. Here, we wish to marry these two perspectives and understand whether and in which form the information-theoretic lens can “see” the effect of symmetries of a system. For this purpose, we propose a novel variant of the Information Bottleneck prin- ciple, which has served as a productive basis for many principled studies of learning and information-constrained adaptive behaviour. We show (in the discrete case) that our ap- proach formalises a certain duality between symmetry and information parsimony: namely, channel equivariances can be characterised by the optimal mutual information-preserving joint compression of the channel’s input and output. This information-theoretic treatment furthermore suggests a principled notion of “soft” equivariance, whose “coarseness” is mea- sured by the amount of input-output mutual information preserved by the corresponding optimal compression. This new notion offers a bridge between the field of bounded ratio- nality and the study of symmetries in neural representations. The framework may also allow (exact and soft) equivariances to be automatically discovered.Peer reviewe

Towards Information Theory-Based Discovery of Equivariances

The presence of symmetries imposes a stringent set of constraints on a system. This constrained structure allows intelligent agents interacting with such a system to drastically improve the efficiency of learning and generalization, through the internalisation of the system's symmetries into their information-processing. In parallel, principled models of complexity-constrained learning and behaviour make increasing use of information-theoretic methods. Here, we wish to marry these two perspectives and understand whether and in which form the information-theoretic lens can "see" the effect of symmetries of a system. For this purpose, we propose a novel variant of the Information Bottleneck principle, which has served as a productive basis for many principled studies of learning and information-constrained adaptive behaviour. We show (in the discrete case) that our approach formalises a certain duality between symmetry and information parsimony: namely, channel equivariances can be characterised by the optimal mutual information-preserving joint compression of the channel's input and output. This information-theoretic treatment furthermore suggests a principled notion of "soft" equivariance, whose "coarseness" is measured by the amount of input-output mutual information preserved by the corresponding optimal compression. This new notion offers a bridge between the field of bounded rationality and the study of symmetries in neural representations. The framework may also allow (exact and soft) equivariances to be automatically discovered.Comment: 23 pages, 0 figure

THE ROLE OF INTEREST RATES AND PROVINCIAL MONETARY AGGREGATE IN MAINTAINING REGIONAL INFLATION IN INDONESIA

In most countries, monetary policies are implemented in order to maintain economic stability. The policy may employ interest rate or money supply to derive the assigned national inflation target. Most studies investigate the relationship between monetary policy and inflation use national data. Based on the idea that inflation is a regional phenomenon, the application of provincial data might be more appropriate explaining the relationship between monetary policy and inflation. The study elaborate the impact of changes in provincial money supply, BI Rate (interest rates of central bank), and PUAB (money market interest rates) to regional inflation in the framework Hybrid New Keynesian Phillips Curve (HNKPC). The study employs Generalized Method of Moments (GMM) techniques on panel data of 32 provinces from 2005-III to 2014-IV. The data is classified into 4 groups, which are Jawa-Bali (W1), Sumatera (W2), Kalimantan-Sulawesi (W3), and Papua-Maluku-Nusa Tenggara (W4). The estimation result shows that provincial monetary aggregate influence inflation significantly only in Sumatera. Furthermore, inflation is also effect by BI Rate in Sumatera and Kalimantan-Sulawesi. The study also found that PUAB is significantly affecting inflation in almost all Indonesian regions, except Kalimantan-Sulawesi. This study concludes that interest rates, BI rate and PUAB, is more appropriate than change in provincial money supply to control provincial inflation.Keywords: monetary policy, regional inflation, hybrid NKPCJEL Classification Numbers: E31, E52, R1

Exact and Soft Successive Refinement of the Information Bottleneck

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The information bottleneck (IB) framework formalises the essential requirement for efficient information processing systems to achieve an optimal balance between the complexity of their representation and the amount of information extracted about relevant features. However, since the representation complexity affordable by real-world systems may vary in time, the processing cost of updating the representations should also be taken into account. A crucial question is thus the extent to which adaptive systems can leverage the information content of already existing IB-optimal representations for producing new ones, which target the same relevant features but at a different granularity. We investigate the information-theoretic optimal limits of this process by studying and extending, within the IB framework, the notion of successive refinement, which describes the ideal situation where no information needs to be discarded for adapting an IB-optimal representation’s granularity. Thanks in particular to a new geometric characterisation, we analytically derive the successive refinability of some specific IB problems (for binary variables, for jointly Gaussian variables, and for the relevancy variable being a deterministic function of the source variable), and provide a linear-programming-based tool to numerically investigate, in the discrete case, the successive refinement of the IB. We then soften this notion into a quantification of the loss of information optimality induced by several-stage processing through an existing measure of unique information. Simple numerical experiments suggest that this quantity is typically low, though not entirely negligible. These results could have important implications for (i) the structure and efficiency of incremental learning in biological and artificial agents, (ii) the comparison of IB-optimal observation channels in statistical decision problems, and (iii) the IB theory of deep neural networks.Peer reviewe