Browsing by Subject "Bayesian optimization"

Chemical reaction optimization is an iterative process that targets identifying reaction conditions that maximize reaction output, typically yield. The evolution of optimization techniques has progressed from intuitive approaches to simple heuristics, and more recently, to statistical methods such as Design of Experiments approach. Bayesian optimization, which iteratively updates beliefs about a response surface and suggests parameters both exploiting conditions near the known optima and exploring uncharted regions, has shown promising results by reducing the number of experiments needed for finding the optimum in various optimization tasks. In chemical reaction optimization, the method allows minimizing the number of experiments required for finding the optimal reaction conditions. Automated tools like pipetting robots hold potential to accelerate optimization by executing multiple reactions concurrently. The integration of Bayesian optimization to automation reduces not only the workload and throughput but also optimization efficiency. However, adoption of these advanced techniques faces a barrier, as chemists often lack proficiency in machine learning and programming. To bridge this gap, Automated Chemical Reaction Optimization Software (ACROS) is introduced. This tool orchestrates an optimization loop: Bayesian optimization suggests reaction candidates, the parameters are translated into commands for a pipetting robot, the robot executes the operations, a chemist interprets the results, and data is fed back to the software for suggesting the next reaction candidates. The optimization tool was evaluated empirically using a numerical test function, in a Direct Arylation reaction dataset, and in real-time optimization of Sonogashira and Suzuki coupling reactions. The findings demonstrate that Bayesian optimization efficiently identifies optimal conditions, outperforming Design of Experiments approach, particularly in optimizing discrete parameters in batch settings. Three acquisition functions; Expected Improvement, Log Expected Improvement and Upper Confidence Bound; were compared. It can be concluded that expected improvement-based methods are more robust, especially in batch settings with process constraints.

Most of the standard statistical inference methods rely on the evaluating so called likelihood functions. But in some cases the phenomenon of interest is too complex or the relevant data inapplicable and as a result the likelihood function cannot be evaluated. Such a situation blocks frequentist methods based on e.g. maximum likelihood estimation and Bayesian inference based on estimating posterior probabilities. Often still, the phenomenon of interest can be modeled with a generative model that describes supposed underlying processes and variables of interest. In such scenarios, likelihood-free inference, such as Approximate Bayesian Computation (ABC), can provide an option for overcoming the roadblock. Creating a simulator that implements such a generative model provides a way to explore the parameter space and approximate the likelihood function based on similarity between real world data and the data simulated with various parameter values. ABC provides well defined and studied framework for carrying out such simulation-based inference with Bayesian approach. ABC has been found useful for example in ecology, finance and astronomy, in situations where likelihood function is not practically computable but models and simulators for generating simulated data are available. One such problem is the estimation of recombination rates of bacterial populations from genetic data, which often is unsuitable for typical statistical methods due to infeasibly massive modeling and computation requirements. Overcoming these hindrances should provide valuable insight into evolution of bacteria and possibly aid in tackling significant challenges such as antimicrobial resistance. Still, ABC inference is not without its limitations either. Often considerable effort in defining distance functions, summary statistics and threshold for similarity is required to make the comparison mechanism successful. High computational costs can also be a hindrance in ABC inference; As increasingly complex phenomena and thus models are studied, the computations that are needed for sufficient exploration of parameter space with the simulation-comparison cycles can get too time- and resource-consuming. Thus efforts have been made to improve the efficiency of ABC inference. One improvement here has been the Bayesian Optimization for Likelihood-Free Inference algorithm (BOLFI), which provides efficient method to optimize the exploration of parameter space, reducing the amount of needed simulation-comparison cycles by up to several magnitudes. This thesis aims to describe some of the theoretical and applied aspects of the complete likelihood-free inference pipelines using both Rejection ABC and BOLFI methods. The thesis presents also use case where the neutral evolution recombination rate in Streptococcus pneumoniae population is inferred from well-studied real world genome data set. This inference task is used to provide context and concrete examples for the theoretical aspects, and demonstrations for numerous applied aspects. The implementations, experiments and acquired results are also discussed in some detail.