假設市場評價法（Contingent valuation）多用於評估有關某一非市場性財產（Non-market goods）或公共財（Public goods）在民眾心目中的願付價值（Willingness to pay, WTP）。探討受訪者願付價值之研究調查案的問卷設計方式，大致可分成五種，其中開放式出價法和逐步競價法已被證實會對估計造成偏誤，而支付卡法、二分選擇法和雙界二分選擇法則是現今較常為研究者所使用的價格詢問方法，本論文的研究是針對二分選擇法的最佳設計（Optimal design），作一深入的探討。 假設欲探究之母體的願付價值為一服從平均數為 、標準差為 的常態分配，若採用二分選擇法作為價格詢問的方式，則何種詢問方法才能讓參數估計最佳化，由模擬實驗的結果，我們知道若將受訪者隨機等分成兩群，分別詢問兩個不同的價格，且這兩個價格的平均等於預先猜測的母體平均數，那麼不但會有相當不錯的估計結果，在實際的執行上亦較方便。此外，我們提出較容易計算的參數估計量來代替傳統的最大概似估計量（MLE），並以數理證明保證了新的參數估計量有良好的估計性質。 願付價值的研究若對母體資訊不充分時，常會先採行前測（Pretest）。本論文除了探討二分選擇法的最佳設計之外，亦針對支付卡法和二分選擇法運用在前測時，作一深入的討論，結果發現當事先猜測的母體分配參數和真實分配相差很多下，支付卡法和二分選擇法會產生無法估計的情況，因此我們提出新的前測方法，試圖彌補這兩種傳統前測法的不足，我們稱之為序列詢問法（Sequence method）。序列詢問法為一種追蹤母體平均數的方式，依照現在這位受訪者的回答，決定下一位受訪者的詢問價格，在我們的研究中發現，如此的序列詢問方法比傳統的前測法利用更少的資訊，但仍然維持不錯的母體平均數估計結果。

Application of the non-invasive methods to detection of the baroreflex sensitivity is a correct way to evaluate the functions of cardiovascular system. This master’s thesis describes the theoretical informations about the problem of baroreflex sensitivity from anatomical, patalogical and clinical views. Theoretical knowledges are foundation for mathematical description of some methods to detection of baroreflx sensitivity in time, frequency and information dimensions. In the practical part of the master’s theses are presented two methods of BRS detection – sequence method based on finding the specific sequences of time series signals and method of application bivariante autoregressive model. Both of methods are implemented in MATLAB. The results of testing data on real data are discussed.

Visual place recognition is the task of finding same places in a set of database images for a given set of query images. This becomes particularly challenging for long-term applications when the environmental condition changes between or within the database and query set, e.g., from day to night. Visual place recognition in changing environments can be used if global position data like GPS is not available or very inaccurate, or for redundancy. It is required for tasks like loop closure detection in SLAM, candidate selection for global localization, or multi-robot/multi-session mapping and map merging. In contrast to pure image retrieval, visual place recognition can often build upon additional information and data for improvements in performance, runtime, or memory usage. This includes additional data-inherent knowledge about information that is contained in the image sets themselves because of the way they were recorded. Using data-inherent knowledge avoids the dependency on other sensors, which increases the generality of methods for an integration into many existing place recognition pipelines. This thesis focuses on the usage of additional data-inherent knowledge. After the discussion of basics about visual place recognition, the thesis gives a systematic overview of existing data-inherent knowledge and corresponding methods. Subsequently, the thesis concentrates on a deeper consideration and exploitation of four different types of additional data-inherent knowledge. This includes 1) sequences, i.e., the database and query set are recorded as spatio-temporal sequences so that consecutive images are also adjacent in the world, 2) knowledge of whether the environmental conditions within the database and query set are constant or continuously changing, 3) intra-database similarities between the database images, and 4) intra-query similarities between the query images. Except for sequences, all types have received only little attention in the literature so far. For the exploitation of knowledge about constant conditions within the database and query set (e.g., database: summer, query: winter), the thesis evaluates different descriptor standardization techniques. For the alternative scenario of continuous condition changes (e.g., database: sunny to rainy, query: sunny to cloudy), the thesis first investigates the qualitative and quantitative impact on the performance of image descriptors. It then proposes and evaluates four unsupervised learning methods, including our novel clustering-based descriptor standardization method K-STD and three PCA-based methods from the literature. To address the high computational effort of descriptor comparisons during place recognition, our novel method EPR for efficient place recognition is proposed. Given a query descriptor, EPR uses sequence information and intra-database similarities to identify nearly all matching descriptors in the database. For a structured combination of several sources of additional knowledge in a single graph, the thesis presents our novel graphical framework for place recognition. After the minimization of the graph's error with our proposed ICM-based optimization, the place recognition performance can be significantly improved. For an extensive experimental evaluation of all methods in this thesis and beyond, a benchmark for visual place recognition in changing environments is presented, which is composed of six datasets with thirty sequence combinations.