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Chiến lược Lấy Mẫu Để Giảm Thiểu Sự Bất Định Trong Các Trường Ngẫu Nhiên Phân Loại: Hình Thành, Phân Tích Toán Học và Ứng Dụng Cho Các Mô Hình Nhiều Điểm
Tóm tắt
Nhiệm vụ lấy mẫu tối ưu cho mô phỏng thống kê của một trường ngẫu nhiên rời rạc được giải quyết từ góc độ giảm thiểu sự bất định hậu nghiệm của các vị trí chưa được cảm biến dựa trên thông tin của các vị trí đã được cảm biến. Cụ thể, các biện pháp lý thuyết thông tin được áp dụng để hình thức hóa vấn đề thiết kế lấy mẫu tối ưu cho việc đặc trưng hóa trường, nơi mà các khái niệm như thông tin của các phép đo, sự bất định hậu nghiệm trung bình, và khả năng giải quyết của trường được giới thiệu. Việc sử dụng entropy và các biện pháp thông tin liên quan được xác nhận bằng cách kết nối nhiệm vụ mô phỏng với một vấn đề mã hóa nguồn, trong đó đã được biết rằng entropy cung cấp một giới hạn hiệu suất cơ bản. Trong ứng dụng, một mô hình chuỗi Markov một chiều được khám phá nơi mà thống kê của đối tượng ngẫu nhiên được biết, và sau đó trường hợp liên quan hơn của các mô phỏng nhiều điểm của các trường facies kênh được nghiên cứu, áp dụng trong trường hợp này một hình ảnh huấn luyện để suy diễn thống kê của một mô hình không tham số. Trong cả hai bối cảnh, sự vượt trội của các chiến lược lấy mẫu dựa trên thông tin đã được chứng minh trong các môi trường và điều kiện khác nhau, so với việc lấy mẫu ngẫu nhiên hoặc đều.
Từ khóa
#Lấy mẫu tối ưu #trường ngẫu nhiên phân loại #sự bất định hậu nghiệm #chiến lược lấy mẫu dựa trên thông tin #mô phỏng nhiều điểm.Tài liệu tham khảo
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