Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Sự suy diễn và tính toán với các mô hình cộng sinh tổng quát và các mở rộng của chúng
Tóm tắt
Các mô hình hồi quy mà trong đó một biến phản hồi liên quan đến các hàm mượt mà của một số biến dự đoán đang ngày càng trở nên phổ biến nhờ vào sự cân bằng hấp dẫn giữa tính linh hoạt và khả năng giải thích. Kể từ khi các mô hình cộng sinh tổng quát ban đầu của Hastie và Tibshirani (Mô hình cộng sinh tổng quát. Chapman & Hall, Boca Raton, 1990), nhiều mở rộng mô hình đã được đề xuất, và nhiều chiến lược tính toán thực tiễn hữu ích đã xuất hiện. Bài báo này cung cấp một cái nhìn tổng quan về một số khuôn khổ có thể áp dụng rộng rãi cho loại mô hình này, nhấn mạnh sự tương đồng giữa các phương pháp khác nhau, cũng như sự tương đương của mượt hóa, mô hình quá trình ẩn Gauss và hiệu ứng ngẫu nhiên Gauss. Trọng tâm đặc biệt là lý thuyết mượt hóa Bayes thực nghiệm, suy diễn Bayes đầy đủ thông qua mô phỏng ngẫu nhiên hoặc xấp xỉ Laplace tổ hợp lồng ghép và tăng cường.
Từ khóa
#mô hình cộng sinh tổng quát #mượt hóa #suy diễn Bayes #mô phỏng ngẫu nhiên #xấp xỉ LaplaceTài liệu tham khảo
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