Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

Statistical Model to DNA Melting

We explore a DNA statistical model to obtain information about the behavior of the thermodynamics quantities. Special attention is given to the thermal denaturation of this macromolecule.

The Role of Universities in the Triple Helix model of innovation management within the biotechnology industry, Case example: Turku Science Park Network

Biotechnology has been recognized as the key strategic technology for industrial growth. The industry is heavily dependent on basic research. Finland continues to rank in the top 10 of Europe's most innovative countries in terms of tax-policy, education system, infrastructure and the number of patents issued. Regardless of the excellent statistical results, the output of this innovativeness is below acceptable. Research on the issues hindering the output creation has already been done and the identifiable weaknesses in the Finland's National Innovation system are the non-existent growth of entrepreneurship and the missing internationalization. Finland is proven to have all the enablers of the innovation policy tools, but is lacking the incentives and rewards to push the enablers, such as knowledge and human capital, forward. Science Parks are the biggest operator in research institutes in the Finnish Science and Technology system. They exist with the purpose of speeding up the commercialization process of biotechnology innovations which usually include technological uncertainty, technical inexperience, business inexperience and high technology cost. Innovation management only internally is a rather historic approach, current trend drives towards open innovation model with strong triple helix linkages. The evident problems in the innovation management within the biotechnology industry are examined through a case study approach including analysis of the semi-structured interviews which included biotechnology and business expertise from Turku School of Economics. The results from the interviews supported the theoretical implications as well as conclusions derived from the pilot survey, which focused on the companies inside Turku Science Park network. One major issue that the Finland's National innovation system is struggling with is the fact that it is technology driven, not business pulled. Another problem is the university evaluation scale which focuses more on number of graduates and short-term factors, when it should put more emphasis on the cooperation success in the long-term, such as the triple helix connections with interaction and knowledge distribution. The results of this thesis indicated that there is indeed requirement for some structural changes in the Finland's National innovation system and innovation policy in order to generate successful biotechnology companies and innovation output. There is lack of joint output and scales of success, lack of people with experience, lack of language skills, lack of business knowledge and lack of growth companies.

The effects of language complexity in a counterattitudinal appeal to laypeople : an application of the elaboration likelihood model of persuasion to a legal issue

The effects of a complexly worded counterattitudinal appeal on laypeople's attitudes toward a legal issue were examined, using the Elaboration Likelihood Model (ELM) of persuasion as a theoretical framework. This model states that persuasion can result from the elaboration and scrutiny of the message arguments (i.e., central route processing), or can result from less cognitively effortful strategies, such as relying on source characteristics as a cue to message validity (i.e., peripheral route processing). One hundred and sixty-seven undergraduates (85 men and 81 women) listened to eitller a low status or high status source deliver a counterattitudinal speech on a legal issue. The speech was designed to contain strong or weak arguments. These arguments were 'worded in a simple and, therefore, easy to comprehend manner, or in a complex and, therefore, difficult to comprehend manner. Thus, there were three experimental manipulations: argument comprehensibility (easy to comprehend vs. difficult to comprehend), argumel11 strength (weak vs. strong), and source status (low vs. high). After listening to tIle speec.J] participants completed a measure 'of their attitude toward the legal issue, a thought listil1g task, an argument recall task,manipulation checks, measures of motivation to process the message, and measures of mood. As a result of the failure of the argument strength manipulation, only the effects of the comprehel1sibility and source status manipulations were tested. There was, however, some evidence of more central route processing in the easy comprehension condition than in the difficult comprehension condition, as predicted. Significant correlations were found between attitude and favourable and unfavourable thoughts about the legal issue with easy to comprehend arguments; whereas, there was a correlation only between attitude and favourable thoughts 11 toward the issue with difficult to comprehend arguments, suggesting, perhaps, that central route processing, \vhich involves argument scrutiny and elaboration, occurred under conditions of easy comprehension to a greater extent than under conditions of difficult comprehension. The results also revealed, among other findings, several significant effects of gender. Men had more favourable attitudes toward the legal issue than did women, men recalled more arguments from the speech than did women, men were less frustrated while listening to the speech than were ,vomen, and men put more effort into thinking about the message arguments than did women. When the arguments were difficult to comprehend, men had more favourable thoughts and fewer unfavourable thoughts about the legal issue than did women. Men and women may have had different affective responses to the issue of plea bargaining (with women responding more negatively than men), especially in light of a local and controversial plea bargain that occurred around the time of this study. Such pre-existing gender differences may have led to tIle lower frustration, the greater effort, the greater recall, and more positive attitudes for men than for WOlnen. Results· from this study suggest that current cognitive models of persuasion may not be very applicable to controversial issues which elicit strong emotional responses. Finally, these data indicate that affective responses, the controversial and emotional nature ofthe issue, gender and other individual differences are important considerations when experts are attempting to persuade laypeople toward a counterattitudinal position.

Statistical Inference for Computable General Equilibrium Models with Application to a Model of the Moroccan Economy

We study the problem of measuring the uncertainty of CGE (or RBC)-type model simulations associated with parameter uncertainty. We describe two approaches for building confidence sets on model endogenous variables. The first one uses a standard Wald-type statistic. The second approach assumes that a confidence set (sampling or Bayesian) is available for the free parameters, from which confidence sets are derived by a projection technique. The latter has two advantages: first, confidence set validity is not affected by model nonlinearities; second, we can easily build simultaneous confidence intervals for an unlimited number of variables. We study conditions under which these confidence sets take the form of intervals and show they can be implemented using standard methods for solving CGE models. We present an application to a CGE model of the Moroccan economy to study the effects of policy-induced increases of transfers from Moroccan expatriates.

Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.

English to Malayalam Translation: A Statistical Approach

This paper underlines a methodology for translating text from English into the Dravidian language, Malayalam using statistical models. By using a monolingual Malayalam corpus and a bilingual English/Malayalam corpus in the training phase, the machine automatically generates Malayalam translations of English sentences. This paper also discusses a technique to improve the alignment model by incorporating the parts of speech information into the bilingual corpus. Removing the insignificant alignments from the sentence pairs by this approach has ensured better training results. Pre-processing techniques like suffix separation from the Malayalam corpus and stop word elimination from the bilingual corpus also proved to be effective in training. Various handcrafted rules designed for the suffix separation process which can be used as a guideline in implementing suffix separation in Malayalam language are also presented in this paper. The structural difference between the English Malayalam pair is resolved in the decoder by applying the order conversion rules. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

A framework for translating English text into Malayalam using statistical models

A methodology for translating text from English into the Dravidian language, Malayalam using statistical models is discussed in this paper. The translator utilizes a monolingual Malayalam corpus and a bilingual English/Malayalam corpus in the training phase and generates automatically the Malayalam translation of an unseen English sentence. Various techniques to improve the alignment model by incorporating the morphological inputs into the bilingual corpus are discussed. Removing the insignificant alignments from the sentence pairs by this approach has ensured better training results. Pre-processing techniques like suffix separation from the Malayalam corpus and stop word elimination from the bilingual corpus also proved to be effective in producing better alignments. Difficulties in translation process that arise due to the structural difference between the English Malayalam pair is resolved in the decoding phase by applying the order conversion rules. The handcrafted rules designed for the suffix separation process which can be used as a guideline in implementing suffix separation in Malayalam language are also presented in this paper. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

Handling OOV Words in Phrase-Based Statistical Machine Translation for Malayalam

Statistical Machine Translation (SMT) is one of the potential applications in the field of Natural Language Processing. The translation process in SMT is carried out by acquiring translation rules automatically from the parallel corpora. However, for many language pairs (e.g. Malayalam- English), they are available only in very limited quantities. Therefore, for these language pairs a huge portion of phrases encountered at run-time will be unknown. This paper focuses on methods for handling such out-of-vocabulary (OOV) words in Malayalam that cannot be translated to English using conventional phrase-based statistical machine translation systems. The OOV words in the source sentence are pre-processed to obtain the root word and its suffix. Different inflected forms of the OOV root are generated and a match is looked up for the word variants in the phrase translation table of the translation model. A Vocabulary filter is used to choose the best among the translations of these word variants by finding the unigram count. A match for the OOV suffix is also looked up in the phrase entries and the target translations are filtered out. Structuring of the filtered phrases is done and SMT translation model is extended by adding OOV with its new phrase translations. By the results of the manual evaluation done it is observed that amount of OOV words in the input has been reduced considerably

A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference

We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

A Dynamical Systems Model for Language Change

Formalizing linguists' intuitions of language change as a dynamical system, we quantify the time course of language change including sudden vs. gradual changes in languages. We apply the computer model to the historical loss of Verb Second from Old French to modern French, showing that otherwise adequate grammatical theories can fail our new evolutionary criterion.

Statistical Models for Co-occurrence Data

Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from two finite sets. The main challenge for statistical models in this context is to overcome the inherent data sparseness and to estimate the probabilities for pairs which were rarely observed or even unobserved in a given sample set. Moreover, it is often of considerable interest to extract grouping structure or to find a hierarchical data organization. A novel family of mixture models is proposed which explain the observed data by a finite number of shared aspects or clusters. This provides a common framework for statistical inference and structure discovery and also includes several recently proposed models as special cases. Adopting the maximum likelihood principle, EM algorithms are derived to fit the model parameters. We develop improved versions of EM which largely avoid overfitting problems and overcome the inherent locality of EM--based optimization. Among the broad variety of possible applications, e.g., in information retrieval, natural language processing, data mining, and computer vision, we have chosen document retrieval, the statistical analysis of noun/adjective co-occurrence and the unsupervised segmentation of textured images to test and evaluate the proposed algorithms.

Nuclear magnetic resonance chemical shifts with the statistical average of orbital-dependent model potentials in Kohn-Sham density functional theory

The performance of the SAOP potential for the calculation of NMR chemical shifts was evaluated. SAOP results show considerable improvement with respect to previous potentials, like VWN or BP86, at least for the carbon, nitrogen, oxygen, and fluorine chemical shifts. Furthermore, a few NMR calculations carried out on third period atoms (S, P, and Cl) improved when using the SAOP potential

An Assessment of the Recent Past and Future Climate Change, Glacier Retreat, and Runoff in the Caucasus Region Using Dynamical and Statistical Downscaling and HBV-ETH Hydrological Model

The paper discusses the observed and projected warming in the Caucasus region and its implications for glacier melt and runoff. A strong positive trend in summer air temperatures of 0.05 degrees C a(-1) is observed in the high-altitude areas providing for a strong glacier melt and continuous decline in glacier mass balance. A warming of 4-7 degrees C and 3-5 degrees C is projected for the summer months in 2071-2100 under the A2 and B2 emission scenarios respectively, suggesting that enhanced glacier melt can be expected. The expected changes in winter precipitation will not compensate for the summer melt and glacier retreat is likely to continue. However, a projected small increase in both winter and summer precipitation combined with the enhanced glacier melt will result in increased summer runoff in the currently glaciated region of the Caucasus (independent of whether the region is glaciated at the end of the twenty-first century) by more than 50% compared with the baseline period.

Diagnosis of variability and trends in a global precipitation dataset using a physically motivated statistical model

A physically motivated statistical model is used to diagnose variability and trends in wintertime ( October - March) Global Precipitation Climatology Project (GPCP) pentad (5-day mean) precipitation. Quasi-geostrophic theory suggests that extratropical precipitation amounts should depend multiplicatively on the pressure gradient, saturation specific humidity, and the meridional temperature gradient. This physical insight has been used to guide the development of a suitable statistical model for precipitation using a mixture of generalized linear models: a logistic model for the binary occurrence of precipitation and a Gamma distribution model for the wet day precipitation amount. The statistical model allows for the investigation of the role of each factor in determining variations and long-term trends. Saturation specific humidity q(s) has a generally negative effect on global precipitation occurrence and with the tropical wet pentad precipitation amount, but has a positive relationship with the pentad precipitation amount at mid- and high latitudes. The North Atlantic Oscillation, a proxy for the meridional temperature gradient, is also found to have a statistically significant positive effect on precipitation over much of the Atlantic region. Residual time trends in wet pentad precipitation are extremely sensitive to the choice of the wet pentad threshold because of increasing trends in low-amplitude precipitation pentads; too low a choice of threshold can lead to a spurious decreasing trend in wet pentad precipitation amounts. However, for not too small thresholds, it is found that the meridional temperature gradient is an important factor for explaining part of the long-term trend in Atlantic precipitation.