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VisualisePriors.py
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VisualisePriors.py
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import streamlit as st
# Import custom functions
from prior_functions import *
# -------------------------- TOP OF PAGE INFORMATION -------------------------
# Set browser / tab config
st.set_page_config(
page_title="MMM App - Prior Distributions Transformations",
page_icon="💎",
)
# Give some context for what the page displays
st.title('Bayesian Prior Distribution Demonstrator')
# Use tabs for different distributions
tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9, tab10, tab11, tab12 = st.tabs([
"🔵 Uniform", "🟣 Normal", "🟠 HalfNormal", "🟢 Beta",
"🔴 Gamma", "⚪ Poisson", "🌈 Bernoulli", "🔵 Exponential",
"🟣 Weibull", "🟠 TruncatedNormal", "🟢 StudentT", "🔴 LogNormal"
])
# Set seed
seed = 42
# -------------------------- UNIFORM DISTRIBUTION -------------------------
with tab1:
st.header(":blue[Uniform Distribution]")
# User inputs for distribution parameters
st.subheader(':blue[User Inputs]')
lower = st.number_input('Lower Bound', value=0.0)
upper = st.number_input('Upper Bound', value=1.0)
# Check to ensure lower < upper
if lower >= upper:
st.error("Error: Lower bound must be less than upper bound.")
else:
# Draw samples
samples = draw_samples_from_prior(pm.Uniform, lower=lower, upper=upper, seed=seed)
# Plot distribution
fig_root = plot_prior_distribution(samples, title="Uniform Distribution Samples")
fig_root.update_layout(height=500, width=1000)
st.plotly_chart(fig_root, use_container_width=True)
# -------------------------- NORMAL DISTRIBUTION -------------------------
with tab2:
st.header(":violet[Normal Distribution]")
# User inputs for distribution parameters
st.subheader(':violet[User Inputs]')
mu = st.number_input('Mean (mu)', value=0.0)
sigma = st.number_input('Standard Deviation (sigma)', value=1.0, min_value=0.01)
# Draw samples
samples = draw_samples_from_prior(pm.Normal, mu=mu, sigma=sigma, seed=seed)
# Plot distribution
fig = plot_prior_distribution(samples, title="Normal Distribution Samples")
fig.update_layout(height=500, width=1000)
st.plotly_chart(fig, use_container_width=True)
# -------------------------- HALF-NORMAL DISTRIBUTION -------------------------
with tab3:
st.header(":orange[HalfNormal Distribution]")
st.subheader(':orange[User Inputs]')
sigma = st.number_input('Standard Deviation (sigma) for HalfNormal', value=1.0, min_value=0.01)
# Draw samples
samples = draw_samples_from_prior(pm.HalfNormal, sigma=sigma, seed=seed)
# Plot distribution
fig = plot_prior_distribution(samples, title="HalfNormal Distribution Samples")
fig.update_layout(height=500, width=1000)
st.plotly_chart(fig, use_container_width=True)
# -------------------------- BETA DISTRIBUTION -------------------------
with tab4:
st.header(":green[Beta Distribution]")
st.subheader(':green[User Inputs]')
alpha = st.number_input('Alpha', value=1.0, min_value=0.01)
beta = st.number_input('Beta', value=1.0, min_value=0.01)
# Draw samples
samples = draw_samples_from_prior(pm.Beta, alpha=alpha, beta=beta, seed=seed)
# Plot distribution
fig = plot_prior_distribution(samples, title="Beta Distribution Samples")
fig.update_layout(height=500, width=1000)
st.plotly_chart(fig, use_container_width=True)
# -------------------------- GAMMA DISTRIBUTION -------------------------
with tab5:
st.header(":red[Gamma Distribution]")
st.subheader(':red[User Inputs]')
alpha = st.number_input('Alpha for Gamma', value=1.0, min_value=0.01)
beta = st.number_input('Beta for Gamma (rate)', value=1.0, min_value=0.01)
# Draw samples
samples = draw_samples_from_prior(pm.Gamma, alpha=alpha, beta=beta, seed=seed)
# Plot distribution
fig = plot_prior_distribution(samples, title="Gamma Distribution Samples")
fig.update_layout(height=500, width=1000)
st.plotly_chart(fig, use_container_width=True)
# -------------------------- POISSON DISTRIBUTION -------------------------
with tab6:
st.header(":grey[Poisson Distribution]")
st.subheader(':grey[User Inputs]')
mu = st.number_input('Lambda (mu)', value=1.0, min_value=0.01)
# Draw samples
samples = draw_samples_from_prior(pm.Poisson, mu=mu, seed=seed)
# Plot distribution
fig = plot_prior_distribution(samples, title="Poisson Distribution Samples")
fig.update_layout(height=500, width=1000)
st.plotly_chart(fig, use_container_width=True)
# -------------------------- BERNOULLI DISTRIBUTION -------------------------
with tab7:
st.header(":rainbow[Bernoulli Distribution]")
st.subheader(':rainbow[User Inputs]')
p = st.number_input('Probability of Success (p)', value=0.5, min_value=0.0, max_value=1.0)
# Draw samples
samples = draw_samples_from_prior(pm.Bernoulli, p=p, seed=seed)
# Plot distribution
fig = plot_prior_distribution(samples, title="Bernoulli Distribution Samples")
fig.update_layout(height=500, width=1000)
st.plotly_chart(fig, use_container_width=True)
# -------------------------- EXPONENTIAL DISTRIBUTION -------------------------
with tab8:
st.header(":blue[Exponential Distribution]")
st.subheader(':blue[User Inputs]')
lam = st.number_input('Rate (lambda)', value=1.0, min_value=0.01)
# Draw samples
samples = draw_samples_from_prior(pm.Exponential, lam=lam, seed=seed)
# Plot distribution
fig = plot_prior_distribution(samples, title="Exponential Distribution Samples")
fig.update_layout(height=500, width=1000)
st.plotly_chart(fig, use_container_width=True)
# -------------------------- WEIBULL DISTRIBUTION ---------------------------
with tab9:
st.header(":violet[Weibull Distribution]")
st.subheader(':violet[User Inputs]')
alpha = st.number_input('Shape (alpha)', value=1.5, min_value=0.01)
beta = st.number_input('Scale (beta)', value=1.0, min_value=0.01)
samples = draw_samples_from_prior(pm.Weibull, alpha=alpha, beta=beta, seed=seed)
fig = plot_prior_distribution(samples, title="Weibull Distribution Samples")
st.plotly_chart(fig, use_container_width=True)
# -------------------------- TRUNCATED NORMAL DISTRIBUTION ------------------
with tab10:
st.header(":orange[TruncatedNormal Distribution]")
st.subheader(':orange[User Inputs]')
mu = st.number_input('Mean (mu) for TruncatedNormal', value=0.0)
sigma = st.number_input('Standard Deviation (sigma) for TruncatedNormal', value=1.0, min_value=0.01)
lower = st.number_input('Lower Bound for TruncatedNormal', value=0.0)
upper = st.number_input('Upper Bound for TruncatedNormal', value=2.0)
# Check to ensure lower < upper
if lower >= upper:
st.error("Error: Lower bound must be less than upper bound.")
else:
samples = draw_samples_from_prior(pm.TruncatedNormal, mu=mu, sigma=sigma, lower=lower, upper=upper, seed=seed)
fig = plot_prior_distribution(samples, title="TruncatedNormal Distribution Samples")
st.plotly_chart(fig, use_container_width=True)
# -------------------------- STUDENT T DISTRIBUTION -------------------------
with tab11:
st.header(":green[StudentT Distribution]")
st.subheader(':green[User Inputs]')
nu = st.number_input('Degrees of Freedom (nu)', value=10.0, min_value=0.01)
mu = st.number_input('Location (mu)', value=0.0)
sigma = st.number_input('Scale (sigma)', value=1.0, min_value=0.01)
samples = draw_samples_from_prior(pm.StudentT, nu=nu, mu=mu, sigma=sigma, seed=seed)
fig = plot_prior_distribution(samples, title="StudentT Distribution Samples")
st.plotly_chart(fig, use_container_width=True)
# -------------------------- LOGNORMAL DISTRIBUTION -------------------------
with tab12:
st.header(":red[LogNormal Distribution]")
st.subheader(':red[User Inputs]')
mu = st.number_input('Mean (mu) for LogNormal', value=0.0)
sigma = st.number_input('Standard Deviation (sigma) for LogNormal', value=1.0, min_value=0.01)
samples = draw_samples_from_prior(pm.Lognormal, mu=mu, sigma=sigma, seed=seed)
fig = plot_prior_distribution(samples, title="LogNormal Distribution Samples")
st.plotly_chart(fig, use_container_width=True)