Graduate course on the theory of detection and estimation. Hypothesis testing: Bayesian, minimax and Neyman-Pearson approaches. Signal detection in discrete time: Detector structures and performance evaluation. Parameter estimation: Bayesian estimation, nonrandom parameter estimation, maximum likelihood estimation. Signal estimation in discrete time: Linear estimation theory and Kalman-Bucy filtering.
İlk dosyayı sen ekleyebilirsin — notlar, geçmiş finaller, çözümler, cheat-sheet, ne varsa. Drive linki / PDF / ZIP / fotoğraf, hepsi olur.
Şu an: mail at, ben düzenleyip yayına alayım. Form/upload UX yakında geliyor (Kimya tasarlıyor).
| Dönem | Course CPA | |
|---|---|---|
| 2025-2026 Fall | 3.53 | 1 sec · 28 öğr |
| 2023-2024 Spring | 3.33 | 1 sec · 33 öğr |
| 2022-2023 Spring | 3.43 | 1 sec · 16 öğr |
| 2021-2022 Spring | 3.31 | 1 sec · 12 öğr |
| 2020-2021 Spring | 3.44 | 1 sec · 15 öğr |
| 2019-2020 Fall | 3.55 | 1 sec · 18 öğr |
| 2018-2019 Fall | 3.53 | 1 sec · 19 öğr |
| 2017-2018 Fall | 3.52 | 1 sec · 20 öğr |
| 2016-2017 Fall | 3.53 | 1 sec · 22 öğr |
| 2015-2016 Fall | 3.45 | 1 sec · 25 öğr |
Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.
Course Learning Outcomes: Course Learning Outcome Assessment Construct a hypothesis testing problem; specify the probability distributions of the observations under each hypothesis; formulate optimal decision rules according to various criteria Homework Midterm Final Apply the Bayesian, minimax or Neyman-Pearson approaches to design optimal decision rules; assess the Bayes risk, minimax risk, detection probability, and false-alarm probability. Homework Project Midterm Final Apply detection theor
Introduction, Bayesian hypothesis testing (HT) Bayesian HT, Minimax HT Neyman-Pearson HT, Composite HT Composite HT Signal detection in discrete time Signal detection in discrete time Advanced detection techniques Bayesian parameter estimation Non-random parameter estimation Non-random parameter estimation Maximum likelihood estimation Maximum likelihood estimation Kalman-Bucy filtering (if time) Project Presentations ECTS - Workload Table: Activities Number Hours Workload Final exam 1 3 3 Preparation for Final exam 1 25 25 Course hours 14 3 42 Preparation for Midterm exam 1 20 20 Project (including preparation and presentation if applicable) 1 40 40 Homework 4 5 20 Midterm exam 1 2 2 Total Workload: 152 Total Workload / 30: 152 / 30 5.07 ECTS Credits of the Course: 5 Type of Course: Lecture - Project Course Material: PC - Written Teaching Methods: Lecture - Exercises - Assignment