11.4 Bayesian approaches to experimental design

Bayesian approaches offer a unique perspective on experimental design, combining prior knowledge with observed data to update beliefs. This method allows researchers to quantify uncertainty and make probabilistic inferences about parameters and hypotheses.

Bayesian techniques include model selection, hypothesis testing, and computational methods like MCMC. These tools enable more nuanced analysis of experimental data, providing researchers with powerful ways to interpret results and make informed decisions.

Bayesian Fundamentals

Bayesian Inference and Distributions

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• Bayesian inference updates beliefs about parameters or hypotheses based on observed data
• Prior distribution represents initial beliefs or knowledge about parameters before observing data
  • Can be based on previous studies, expert opinion, or theoretical considerations
  • Example: Assuming a normal distribution with a mean of 0 and variance of 1 for a parameter
• Posterior distribution represents updated beliefs about parameters after observing data
  • Combines prior distribution with likelihood function using Bayes' theorem
  • Provides a complete description of the uncertainty about the parameters given the data
  • Example: After observing data, the posterior distribution may have a mean of 0.5 and variance of 0.8
• Likelihood function measures the probability of observing the data given the parameter values
  • Quantifies how well the model fits the observed data
  • Used to update the prior distribution to obtain the posterior distribution

Credible Intervals

• Credible intervals are the Bayesian equivalent of confidence intervals
• Represent the range of parameter values that have a specified probability of containing the true parameter value
  • Example: A 95% credible interval means there is a 95% probability that the true parameter value lies within that interval
• Derived from the posterior distribution, taking into account both prior information and observed data
• Provide a intuitive and direct interpretation of the uncertainty about the parameters
• Can be asymmetric and depend on the shape of the posterior distribution

Bayesian Model Selection

Bayesian Model Comparison and Bayes Factor

• Bayesian model comparison evaluates the relative support for different models given the data
• Compares the marginal likelihood of each model, which is the probability of the data under the model averaged over all possible parameter values
• Bayes factor is a ratio of the marginal likelihoods of two models
  • Quantifies the relative evidence in favor of one model over another
  • A Bayes factor greater than 1 indicates support for the numerator model, while a Bayes factor less than 1 indicates support for the denominator model
  • Example: A Bayes factor of 10 means that the data are 10 times more likely under the numerator model than the denominator model

Bayesian Hypothesis Testing

• Bayesian hypothesis testing assesses the relative support for different hypotheses using the Bayes factor
• Compares the marginal likelihood of the data under each hypothesis
• Can test point hypotheses (specific parameter values) or composite hypotheses (ranges of parameter values)
• Provides a direct measure of the evidence in favor of one hypothesis over another
• Allows for the incorporation of prior information and updates beliefs based on observed data
• Example: Testing whether a coin is fair (hypothesis 1) or biased (hypothesis 2) based on the number of heads observed in a series of flips

Bayesian Computation

Markov Chain Monte Carlo (MCMC)

• MCMC is a class of algorithms used for sampling from complex probability distributions, such as posterior distributions in Bayesian inference
• Constructs a Markov chain that has the desired distribution as its stationary distribution
• Generates samples from the posterior distribution by simulating the Markov chain for a large number of iterations
• Common MCMC algorithms include Metropolis-Hastings and Gibbs sampling
  • Metropolis-Hastings proposes new parameter values and accepts or rejects them based on a probability ratio
  • Gibbs sampling updates each parameter individually by sampling from its conditional distribution given the current values of the other parameters
• MCMC allows for the estimation of posterior quantities, such as means, variances, and credible intervals, based on the generated samples
• Enables Bayesian inference for complex models where analytical solutions are not available
• Example: Using MCMC to estimate the posterior distribution of the parameters in a hierarchical model with multiple levels of uncertainty