Planning under partial observability by classical replanning: theory and experiments

Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are nvariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.

Prediction of Childhood Asthma Using Conditional Probability and Discrete Event Simulation

Abstract: Asthma prevalence in children and adolescents in Spain is 10-17%. It is the most common chronic illness during childhood. Prevalence has been increasing over the last 40 years and there is considerable evidence that, among other factors, continued exposure to cigarette smoke results in asthma in children. No statistical or simulation model exist to forecast the evolution of childhood asthma in Europe. Such a model needs to incorporate the main risk factors that can be managed by medical authorities, such as tobacco (OR = 1.44), to establish how they affect the present generation of children. A simulation model using conditional probability and discrete event simulation for childhood asthma was developed and validated by simulating realistic scenario. The parameters used for the model (input data) were those found in the bibliography, especially those related to the incidence of smoking in Spain. We also used data from a panel of experts from the Hospital del Mar (Barcelona) related to actual evolution and asthma phenotypes. The results obtained from the simulation established a threshold of a 15-20% smoking population for a reduction in the prevalence of asthma. This is still far from the current level in Spain, where 24% of people smoke. We conclude that more effort must be made to combat smoking and other childhood asthma risk factors, in order to significantly reduce the number of cases. Once completed, this simulation methodology can realistically be used to forecast the evolution of childhood asthma as a function of variation in different risk factors.

Guide to Scholarships and Grants

2015-16 publication highlighting Iowa Scholarships and Grants administered by the Iowa College Student Aid Commission.

Asymptotic equivalence between the unconditional maximum likelihood and the square-law nonlinearity symbol timing estimation

This paper provides a systematic approach to theproblem of nondata aided symbol-timing estimation for linearmodulations. The study is performed under the unconditionalmaximum likelihood framework where the carrier-frequencyerror is included as a nuisance parameter in the mathematicalderivation. The second-order moments of the received signal arefound to be the sufficient statistics for the problem at hand and theyallow the provision of a robust performance in the presence of acarrier-frequency error uncertainty. We particularly focus on theexploitation of the cyclostationary property of linear modulations.This enables us to derive simple and closed-form symbol-timingestimators which are found to be based on the well-known squaretiming recovery method by Oerder and Meyr. Finally, we generalizethe OM method to the case of linear modulations withoffset formats. In this case, the square-law nonlinearity is foundto provide not only the symbol-timing but also the carrier-phaseerror.

Comparison of Pavlovian serial conditional discrimination in rats and hamsters in the same experimental situation

The present study compares behavioral changes between two distinct rodent groups, hamsters (Mesocricetus auratus) and Wistar rats, when submitted in the same homogeneous experimental situations to a serial conditional discrimination procedure which involves water deprivation and the processing of temporal variables. Both hamsters and rats acquired serial positive conditional discrimination as indicated by higher frequencies of magazine-oriented behavior during the tone followed by reinforcement (T+) and preceded by the feature stimulus light (L) and during the empty interval, than during the tone alone not followed by reinforcement (T-). Rats' frequencies of magazine-oriented behavior were high during T+ and T-, initially during training, and decreased during T- as the training progressed. However, the hamsters' frequencies of magazine-oriented behavior started very low and increased only during T+ as the training progressed. Comparison of the frequencies of magazine-oriented behavior during the empty interval in relation to the frequencies during the preceding L period showed that rats' frequencies remained very high and hamsters' frequencies increased during training. These results suggest that rats and hamsters have different behavioral strategies for the acquisition of a conditional discrimination. The results of the comparisons made in these experiments support the view of the importance of an ecological psychology approach to the understanding of complex learning in animals.

Time Varying Conditional Correlation Between Stock And Bond Returns: Evidence From CIVETS Nations

This thesis estimates long-run time variant conditional correlation between stock and bond returns of CIVETS (Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa) nations. Further, aims to analyse the presence of asymmetric volatility effect in both asset returns, as well as, obverses increment or decrement in conditional correlation during pre-crisis and crisis period, which lead to make a reliable diversification decision. The Constant Conditional Correlation (CCC) GARCH model of Bollerslev (1990), the Dynamic Conditional Correlation (DCC) GARCH model (Engle 2002), and the Asymmetric Dynamic Conditional Correlation (ADCC) GARCH model of Cappiello, Engle, and Sheppard (2006) were implemented in the study. The analyses present strong evidence of time-varying conditional correlation in CIVETS markets, excluding Vietnam, during 2005-2013. In addition, negative innovation effects were found in both conditional variance and correlation of the asset returns. The results of this study recommend investors to include financial assets from these markets in portfolios, in order to obtain better stock-bond diversification benefits, especially during high volatility periods.

Russian and Eastern European Dynamic Conditional Correlations in light of the Russian crisis of 2014-2015

This thesis studies the impact of the latest Russian crisis on global markets, and especially Central and Eastern Europe. The results are compared to other shocks and crises over the last twenty years to see how significant they have been. The cointegration process of Central and Eastern European financial markets is also reviewed and updated. Using three separate conditional correlation GARCH models, the latest crisis is not found to have initiated similar surges in conditional correlations to previous crises over the last two decades. Market cointegration for Central and Eastern Europe is found to have stalled somewhat after initial correlation increases post EU accession.

Operant serial feature-positive conditional discrimination with composite features and different topography responses

In a serial feature-positive conditional discrimination procedure the properties of a target stimulus A are defined by the presence or not of a feature stimulus X preceding it. In the present experiment, composite features preceded targets associated with two different topography operant responses (right and left bar pressing); matching and non-matching-to-sample arrangements were also used. Five water-deprived Wistar rats were trained in 6 different trials: X-R®Ar and X-L®Al, in which X and A were same modality visual stimuli and the reinforcement was contingent to pressing either the right (r) or left (l) bar that had the light on during the feature (matching-to-sample); Y-R®Bl and Y-L®Br, in which Y and B were same modality auditory stimuli and the reinforcement was contingent to pressing the bar that had the light off during the feature (non-matching-to-sample); A- and B- alone. After 100 training sessions, the animals were submitted to transfer tests with the targets used plus a new one (auditory click). Average percentages of stimuli with a response were measured. Acquisition occurred completely only for Y-L®Br+; however, complex associations were established along training. Transfer was not complete during the tests since concurrent effects of extinction and response generalization also occurred. Results suggest the use of both simple conditioning and configurational strategies, favoring the most recent theories of conditional discrimination learning. The implications of the use of complex arrangements for discussing these theories are considered.

Conditional Expected Utility

Let 'epsilon' be a class of event. Conditionally Expected Utility decision makers are decision makers whose conditional preferences ≿E, E є 'epsilon', satisfy the axioms of Subjective Expected Utility theory (SEU). We extend the notion of unconditional preference that is conditionally EU to unconditional preferences that are not necessarily SEU. We give a representation theorem for a class of such preferences, and show that they are Invariant Bi-separable in the sense of Ghirardato et al.[7]. Then, we consider the special case where the unconditional preference is itself SEU, and compare our results with those of Fishburn [6].

Conditional compositional biplots: theory and application

The biplot has proved to be a powerful descriptive and analytical tool in many areas of applications of statistics. For compositional data the necessary theoretical adaptation has been provided, with illustrative applications, by Aitchison (1990) and Aitchison and Greenacre (2002). These papers were restricted to the interpretation of simple compositional data sets. In many situations the problem has to be described in some form of conditional modelling. For example, in a clinical trial where interest is in how patients’ steroid metabolite compositions may change as a result of different treatment regimes, interest is in relating the compositions after treatment to the compositions before treatment and the nature of the treatments applied. To study this through a biplot technique requires the development of some form of conditional compositional biplot. This is the purpose of this paper. We choose as a motivating application an analysis of the 1992 US President ial Election, where interest may be in how the three-part composition, the percentage division among the three candidates - Bush, Clinton and Perot - of the presidential vote in each state, depends on the ethnic composition and on the urban-rural composition of the state. The methodology of conditional compositional biplots is first developed and a detailed interpretation of the 1992 US Presidential Election provided. We use a second application involving the conditional variability of tektite mineral compositions with respect to major oxide compositions to demonstrate some hazards of simplistic interpretation of biplots. Finally we conjecture on further possible applications of conditional compositional biplots

On the design of education conditional cash transfer programs and non education outcomes: the case of teenage pregnancy

We investigate the effect of education Conditional Cash Transfer programs (CCTs) on teenage pregnancy. Our main concern is with how the size and sign of the effect may depend on the design of the program. Using a simple model we show that an education CCT that conditions renewal on school performance reduces teenage pregnancy; the program can increase teenage pregnancy if it does not condition on school performance. Then, using an original data base, we estimate the causal impact on teenage pregnancy of two education CCTs implemented in Bogot´a (Subsidio Educativo, SE, and Familias en Acci´on, FA); both programs differ particularly on whether school success is a condition for renewal or not. We show that SE has negative average effect on teenage pregnancy while FA has a null average effect. We also find that SE has either null or no effect for adolescents in all age and grade groups while FA has positive, null or negative effects for adolescents in different age and grade groups. Since SE conditions renewal on school success and FA does not, we can argue that the empirical results are consistent with the predictions of our model and that conditioning renewal of the subsidy on school success crucially determines the effect of the subsidy on teenage pregnancy

Stock return comovements and integration within the Latin American integrated market

Financial integration has been pursued aggressively across the globe in the last fifty years; however, there is no conclusive evidence on the diversification gains (or losses) of such efforts. These gains (or losses) are related to the degree of comovements and synchronization among increasingly integrated global markets. We quantify the degree of comovements within the integrated Latin American market (MILA). We use dynamic correlation models to quantify comovements across securities as well as a direct integration measure. Our results show an increase in comovements when we look at the country indexes, however, the increase in the trend of correlation is previous to the institutional efforts to establish an integrated market in the region. On the other hand, when we look at sector indexes and an integration measure, we find a decreased in comovements among a representative sample of securities form the integrated market.

Training conditional and cumulative risk judgements: the role of frequencies, problem-structure and Einstellung

Two experiments implement and evaluate a training scheme for learning to apply frequency formats to probability judgements couched in terms of percentages. Results indicate that both conditional and cumulative probability judgements can be improved in this manner, however the scheme is insufficient to promote any deeper understanding of the problem structure. In both experiments, training on one problem type only (either conditional or cumulative risk judgements) resulted in an inappropriate transfer of a learned method at test. The obstacles facing a frequency-based training programme for teaching appropriate use of probability data are discussed. Copyright (c) 2006 John Wiley & Sons, Ltd.