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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Ripley’s K and Besag’s L Function
Published:
Summary: This note introduces Ripley’s $K$ and Besag’s $L$ functions for analyzing spatial point patterns. It explains CSR as the null model, defines and estimates $K(r)$ with edge corrections, variance-stabilization via $L(r)$, and the need for Monte Carlo envelopes. Redwood data illustrate clustering versus intensity-driven inhomogeneity.
Understanding of Logistic Regression
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Summary: This note explains why logistic regression is suited for classification. Unlike linear regression, probabilities must remain between 0 and 1. The logistic function enforces this constraint, mapping linear predictors into valid probabilities. Its log-odds formulation ensures interpretability, making logistic regression a well-defined, probabilistic approach for binary classification tasks.
Shell File Descriptor and Redirection Reference
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Summary: This note explains shell file descriptors and redirection. It covers stdin, stdout, stderr, and how to redirect or merge them with operators like >, 2>, and 2>&1. It shows differences between & in redirection and background execution, common command flags, pitfalls, examples, and best practices for HPC logging.
Difference between Standard Deviation and Standard Error
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Summary: This note clarifies the distinction between standard deviation and standard error. Standard deviation measures variability within a dataset, serving as a descriptive statistic, while standard error measures variability of sample means across repeated samples, serving as an inferential statistic. Derivations include Bessel’s correction for unbiased variance estimation and the SE formula.
Another way of understanding Stochastic Gradient Descending (SGD) Algorithm
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Summary: This note reinterprets stochastic gradient descent by modeling sample gradients as random variables. It derives the sample covariance of gradients and shows that the mini-batch gradient is an unbiased estimator of the full gradient, with variance scaling as $\frac{1}{m}$. This variance reflects stochastic noise around the true gradient direction.
Variational Inference
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Summary: This note explains variational inference (VI) as a scalable alternative to MCMC for approximate Bayesian inference. It introduces entropy, cross-entropy, and KL divergence, then contrasts forward (zero-avoiding) vs. reverse (zero-forcing) KL. The ELBO is derived as a tractable optimization target, enabling posterior approximation via Monte Carlo estimation, reparameterization, and mini-batching.
Matrix-Structured Parameterization in MCMC for Accelerated Hypertension Prevalence Modeling
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Summary: This report presents a simpler way to format Bayesian hierarchical model for hypertension prevalence, using Singapore cohort data. It applies matrix-structured parameterization in MCMC to replace redundant intermediate variables from spline expansions, accelerating computation. This reduces runtime from 60 to 5 hours while maintaining convergence and accuracy, enabling efficient large-scale hypertension prevalence modeling.
Understanding of Methodology for Fertility Rate Forecast Model
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Summary: This note records my understanding and supplementary proofs of Schmertmann et al. (2014)’s Bayesian fertility forecasting framework. It details notation, penalty structures for shape and time, covariance prior construction, and likelihood–prior integration. The write-up illustrates posterior fertility forecasts, using Singapore as an example, while emphasizing personal interpretation and derivations.
portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
Published:
Short description of portfolio item number 2
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
Download Paper | Download Slides | Download Bibtex
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
teaching
SPH5002 Public Health Research Methods Weekdays Consultation Sessions
Graduate course, MD1, 2025
| Category | Description | Maximum no. of Sessions / Scripts | Hours per Session / Script | Maximum Hours per Category | Assessment Weightage (if applicable) |
|---|---|---|---|---|---|
| Administrative Work | Answering emails from students with regards to questions. Helping course coordinators with administrative tasks as needed. | - | - | 20 | - |
| Preparation Time for Classroom Teaching | Pre-class preparation for the classroom teaching (TAs may claim preparation time up to 50% of the scheduled class time. For e.g. if a TA is leading a 1.5-hour groupwork session, they can claim up to 0.75 hours of preparation time for that session. Please refer to Section 5.2 of the TA policy for further details) | 2 | 1.5 | 3 | - |
| Classroom Teaching | Conduct review sessions | 2 | 3 | 6 | - |
| Consultations | Provide group consultation sessions outside of lesson time | 6 | 2 | 12 | - |
| Invigilation | Invigilation for 3 quizzes and 1 exam (up to 2.5 hours each) | 4 | 2.5 | 10 | - |
| Total | 51 |