I provide online Bayesian statistics tutoring for graduate students in the Los Angeles and San Francisco Bay Area metros. I regularly work with graduate students from programs at UCLA, USC, UC Irvine, and Caltech, as well as UC Berkeley, Stanford University, UC San Francisco (UCSF), and other UC and private universities. All tutoring is delivered online; I do not maintain a physical office in these cities.
Bayesian statistics appears across biostatistics, public health, economics, data science, psychology, and applied social science graduate programs. Many students understand the mechanics of Bayes’ rule but struggle with prior selection, posterior interpretation, hierarchical modeling, and explaining results in a way that meets graduate-level expectations.
My role is to help you move beyond computation and develop a clear Bayesian workflow— one that you can justify mathematically, explain intuitively, and defend in coursework, qualifying exams, theses, or applied research papers.
Speak Directly With the Tutor
If priors feel arbitrary, posterior results are hard to interpret, or hierarchical Bayes isn’t clicking, reach out directly. You’ll be speaking with the tutor who works through the models with you.
Call/Text: 510-398-0006
Email: tutor@californiagraduatetutor.com
What Bayesian Statistics Tutoring Covers
- Bayes’ theorem and probabilistic reasoning
- Likelihood functions and model specification
- Prior selection: informative vs weakly informative priors
- Posterior distributions and uncertainty
- Bayesian linear and generalized linear models
- Bayesian logistic regression
- Hierarchical and multilevel Bayesian models
- Posterior predictive checks
- Comparing Bayesian and frequentist approaches
Core Bayesian Framework (MathJax Standard)
Bayesian inference updates prior beliefs using observed data:
\[ p(\theta \mid y) = \frac{p(y \mid \theta)\, p(\theta)}{p(y)} \]
Here, \(p(\theta)\) is the prior distribution, \(p(y \mid \theta)\) is the likelihood, and \(p(\theta \mid y)\) is the posterior distribution.
In a Bayesian regression setting:
\[ y_i \mid \beta, \sigma^2 \sim \mathcal{N}(x_i^\top \beta, \sigma^2), \quad \beta \sim \mathcal{N}(0, \Sigma_0) \]
We focus on interpretation: what posterior means and credible intervals actually represent, and how they differ conceptually from confidence intervals taught in frequentist statistics courses.
A Graduate-Level Bayesian Workflow
- State the research question probabilistically
- Specify a likelihood that matches the data-generating process
- Select defensible priors and justify them
- Compute the posterior distribution
- Assess convergence and model adequacy
- Interpret posterior summaries and uncertainty
- Communicate results clearly in writing
Common Graduate Use Cases
- Bayesian regression for applied research papers
- Hierarchical models for clustered or small-sample data
- Bayesian approaches in biostatistics and public health
- Comparing Bayesian and frequentist conclusions
Software Support
- R: Bayesian modeling and diagnostics
- Bayesian workflows aligned with graduate coursework
Related Statistics Support
Need Bayesian Help From the Person Doing the Tutoring?
If Bayesian methods feel abstract or hard to explain, I can help you build intuition, correct models, and a clear graduate-level explanation.
Call/Text: 510-398-0006 | Email: tutor@californiagraduatetutor.com