Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.

Appropriate Institutions and Economic Growth - one size fits no-one

Modern-day economics is increasingly biased towards believing that institutions matter for growth, an argument that has been further enforced by the recent economic crisis. There is also a wide consensus on what these growth-promoting institutions should look like, and countries are periodically ranked depending on how their institutional structure compares with the best-practice institutions, mostly in place in the developing world. In this paper, it is argued that ”non-desirable” or “second-best” institutions can be beneficial for fostering investment and thus providing a starting point for sustained growth, and that what matters is the appropriateness of institutions to the economy’s distance to the frontier or current phase of development. Anecdotal evidence from Japan and South-Korea is used as a motivation for studying the subject and a model is presented to describe this phenomenon. In the model, the rigidity or non-rigidity of the institutions is described by entrepreneurial selection. It is assumed that entrepreneurs are the ones taking part in the imitation and innovation of technologies, and that decisions on whether or not their projects are refinanced comes from capitalists. The capitalists in turn have no entrepreneurial skills and act merely as financers of projects. The model has two periods, and two kinds of entrepreneurs: those with high skills and those with low skills. The society’s choice of whether an imitation or innovation – based strategy is chosen is modeled as the trade-off between refinancing a low-skill entrepreneur or investing in the selection of the entrepreneurs resulting in a larger fraction of high-skill entrepreneurs with the ability to innovate but less total investment. Finally, a real-world example from India is presented as an initial attempt to test the theory. The data from the example is not included in this paper. It is noted that the model may be lacking explanatory power due to difficulties in testing the predictions, but that this should not be seen as a reason to disregard the theory – the solution might lie in developing better tools, not better just better theories. The conclusion presented is that institutions do matter. There is no one-size-fits-all-solution when it comes to institutional arrangements in different countries, and developing countries should be given space to develop their own institutional structures that cater to their specific needs.